A taxonomy is a hierarchical graph containing knowledge to provide valuable insights for various web applications. However, the manual construction of taxonomies requires significant human effort. As web content continues to expand at an unprecedented pace, existing taxonomies risk becoming outdated, struggling to incorporate new and emerging information effectively. As a consequence, there is a growing need for dynamic taxonomy expansion to keep them relevant and up-to-date. Existing taxonomy expansion methods often rely on classical word embeddings to represent entities. However, these embeddings fall short of capturing hierarchical polysemy, where an entity's meaning can vary based on its position in the hierarchy and its surrounding context. To address this challenge, we introduce QuanTaxo, a quantum-inspired framework for taxonomy expansion that encodes entities in a Hilbert space and models interference effects between them, yielding richer, context-sensitive representations. Comprehensive experiments on five real-world benchmark datasets show that QuanTaxo significantly outperforms classical embedding models, achieving substantial improvements of 12.3% in accuracy, 11.2% in Mean Reciprocal Rank (MRR), and 6.9% in Wu & Palmer (Wu&P) metrics across nine classical embedding-based baselines.

Multilingual language models (LMs) organize representations for typologically and orthographically diverse languages into a shared parameter space, yet the nature of this internal organization remains elusive.In this work, we investigate which linguistic properties -abstract language identity or surface-form cues -shape multilingual representations.To do so, we analyze language-associated units across different model families and scales using the Language Activation Probability Entropy (LAPE) metric, and further decompose activations with Sparse Autoencoders.We find that these units are strongly conditioned on orthography: romanization induces near-disjoint representations that align with neither native-script inputs nor English, while word-order shuffling has limited effect on unit identity.Probing shows that typological structure becomes increasingly accessible in deeper layers, while causal interventions indicate that generation is most sensitive to units that are invariant to surface-form perturbations rather than to units identified by typological alignment alone.Overall, our results suggest that multilingual LMs organize representations around surface form, with linguistic abstraction emerging gradually without collapsing into a unified interlingua.

Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Nenkov Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, Preslav Nakov. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Abstract Automatic fact-checking aims to support professional fact-checkers by offering tools that can help speed up manual fact-checking. Yet, existing frameworks fail to address the key step of producing output suitable for broader dissemination to the general public: While human fact-checkers communicate their findings through fact-checking articles, automated systems typically produce little or no justification for their assessments. Here, we aim to bridge this gap. In particular, we argue for the need to extend the typical automatic fact-checking pipeline with automatic generation of full fact-checking articles. We first identify key desiderata for such articles through a series of interviews with experts from leading fact-checking organizations. We then develop Qraft, an LLM-based agentic framework that mimics the writing workflow of human fact-checkers. Finally, we assess the practical usefulness of Qraft through human evaluations with professional fact-checkers. Our evaluation shows that while Qraft outperforms several previously proposed text-generation approaches, it lags considerably behind expert-written articles. We hope that our work will enable further research in this new and important direction. The code for our implementation is available at https://github.com/mbzuai-nlp/qraft.git.

The unwavering disparity in labeled resources between resource-rich languages and those considered low-resource remains a significant impediment for Large Language Models (LLMs). Recent strides in cross-lingual in-context learning (X-ICL), mainly through semantically aligned examples retrieved from multilingual pre-trained transformers, have shown promise in mitigating this issue. However, our investigation reveals that LLMs intrinsically reward in-language semantically aligned cross-lingual instances over direct cross-lingual semantic alignments, with a pronounced disparity in handling time–sensitive queries in the X-ICL setup. Such queries demand sound temporal reasoning ability from LLMs, yet the advancements have predominantly focused on English. This study aims to bridge this gap by improving temporal reasoning capabilities in low-resource languages. To this end, we introduce mTEMPREASON, a temporal reasoning dataset aimed at the varied degrees of low-resource languages and propose Cross-Lingual Time-Sensitive Semantic Alignment (CLiTSSA), a novel method to improve temporal reasoning in these contexts. To facilitate this, we construct an extension of mTEMPREASON comprising pairs of parallel cross–language temporal queries along with their anticipated in-language semantic similarity scores. Our empirical evidence underscores the superior performance of CLiTSSA compared to established baselines across three languages -- Romanian, German, and French, encompassing three temporal tasks and including a diverse set of four contemporaneous LLMs. This marks a significant step forward in addressing resource disparity in the context of temporal reasoning across languages.

Counterspeech has proven to be a powerful tool to combat hate speech online.Previous studies have focused on generating counterspeech conditioned only on specific strategies (single attributed).However, a holistic approach considering multiple attributes simultaneously can yield more nuanced and effective responses.Here, we introduce HiPPrO, Hierarchical Prefix learning with Preference Optimization, a novel two-stage framework that utilizes the effectiveness of attribute-specific prefix embedding spaces hierarchically optimized during the counterspeech generation process in the first phase.Thereafter, we incorporate both reference and reward-free preference optimization to generate more constructive counterspeech.Furthermore, we extend IntentCONANv2 by annotating all 13, 973 counterspeech instances with emotion labels by five annotators.HiPPrO leverages hierarchical prefix optimization to integrate these dual attributes effectively.An extensive evaluation demonstrates that HiPPrO achieves a ∼ 38% improvement in strategy conformity and a ∼ 3%, ∼ 2%, ∼ 3% improvement in Rouge-1, Rouge-2, and Rouge-L, respectively, compared to several baseline models.Human evaluations further substantiate the superiority of our approach, highlighting the enhanced relevance and appropriateness of the generated counterspeech.This work underscores the potential of multiattribute conditioning in advancing the efficacy of counterspeech generation systems. 1 Our code is available on Github and dataset is opensourced on Hugging-face.

The increasing acceptance of large language models (LLMs) as an alternative to knowledge sources marks a significant paradigm shift across various domains, including timesensitive fields such as law, healthcare, and finance.To fulfill this expanded role, LLMs must not only be factually accurate but also demonstrate consistency across temporal dimensions, necessitating robust temporal reasoning capabilities.Despite this critical requirement, efforts to ensure temporal consistency in LLMs remain scarce including noticeable absence of endeavors aimed at evaluating or augmenting LLMs across temporal references in time-sensitive inquiries.In this paper, we seek to address this gap by introducing a novel benchmark entitled temporal referential consistency, accompanied by a resource TEMP-ReCon designed to benchmark a wide range of both open-source and closedsource LLMs with various linguistic contexts characterized by differing resource richness (including English, French, and Romanian).The findings emphasis that LLMs do exhibit insufficient temporal referent consistency.To address this, we propose UnTRaP, a reasoning path alignment-based model that aims to enhance the temporal referential consistency of LLMs.Our empirical experiments substantiate the efficacy of UnTRaP compared to several baseline models.

Large Language Models (LLMs) with safealignment training are powerful instruments with robust language comprehension capabilities.These models typically undergo meticulous alignment procedures involving human feedback to ensure the acceptance of safe inputs while rejecting harmful or unsafe ones.However, despite their massive scale and alignment efforts, LLMs remain vulnerable to jailbreak attacks, where malicious users manipulate the model to produce harmful outputs that it was explicitly trained to avoid.In this study, we find that the safety mechanisms in LLMs are predominantly embedded in the middle-to-late layers.Building on this insight, we introduce a novel white-box jailbreak method, SABER (Safety Alignment Bypass via Extra Residuals), which connects two intermediate layers s and e such that s < e, through a residual connection.Our approach achieves a 51% improvement over the best-performing baseline on the Harm-Bench test set.Furthermore, SABER induces only a marginal shift in perplexity when evaluated on the HarmBench validation set.The source code is publicly available 1 .Warning: This paper contains potentially harmful and offensive content.

Arghodeep Nandi, Megha Sundriyal, Euna Mehnaz Khan, Jikai Sun, Emily K. Vraga, Jaideep Srivastava, Tanmoy Chakraborty. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Most multilingual question-answering benchmarks, while covering a diverse pool of languages, do not factor in regional diversity in the information they capture and tend to be Western-centric.This introduces a significant gap in fairly evaluating multilingual models' comprehension of factual information from diverse geographical locations.To address this, we introduce XNationQA for investigating the cultural literacy of multilingual LLMs.XNationQA encompasses a total of 49, 280 questions on the geography, culture, and history of nine countries, presented in seven languages.We benchmark eight standard multilingual LLMs on XNationQA and evaluate them using two novel transference metrics.Our analyses uncover a considerable discrepancy in the models' accessibility to culturally specific facts across languages.Notably, we often find that a model demonstrates greater knowledge of cultural information in English than in the dominant language of the respective culture.The models exhibit better performance in Western languages, although this does not necessarily translate to being more literate for Western countries, which is counterintuitive.Furthermore, we observe that models have a very limited ability to transfer knowledge across languages, particularly evident in open-source models 1 .

The ever-increasing size of large language models (LLMs) presents significant challenges for deployment due to their heavy computational and memory requirements. Current model pruning techniques attempt to alleviate these issues by relying heavily on external calibration datasets to determine which parameters to prune or compress, thus limiting their flexibility and scalability across different compression ratios. Moreover, these methods often cause severe performance degradation, particularly in downstream tasks, when subjected to higher compression rates. In this paper, we propose *PruneNet*, a novel model compression method that addresses these limitations by reformulating model pruning as a policy learning process. PruneNet decouples the pruning process from the model architecture, eliminating the need for calibration datasets. It learns a stochastic pruning policy to assess parameter importance solely based on intrinsic model properties while preserving the spectral structure to minimize information loss. PruneNet can compress the LLaMA-2-7B model in just 15 minutes, achieving over 80\% retention of its zero-shot performance with a 30\% compression ratio, outperforming existing methods that retain only 75\% performance. Furthermore, on complex multitask language understanding tasks, PruneNet demonstrates its robustness by preserving up to 80\% performance of the original model, proving itself a superior alternative to conventional structured compression techniques.

Suicide accounts for one of the leading causes of death in India, with over 164,033 deaths reported in 2021. Despite increased awareness, the gap between the need for consistent treatment and actual accessibility remains a challenge due to limited mental health infrastructure, the stigma surrounding mental illness in society, and the lack of real-time detection mechanisms. Traditional suicide risk assessments often miss early signs of distress, which rely heavily on clinical evaluations and self-reporting. Although AI-based monitoring seems promising, currently available models focus only on risk prediction without intervention and treatment, leaving a critical gap in tackling crisis management. In this proposal, we strive to design SAHAY, the first-of-its-kind AI-based, suicide prevention framework that seamlessly couples prediction with prevention and treatment access. Leveraging multimodal data, including the social media text and Electronic Health Records (EHR) and Ecological Momentary Assessments (EMA) such as wearable physiological data, SAHAY aims to assess suicide risk dynamically. Unlike existing models, SAHAY is culturally adaptive, multilingual and seamlessly integrates with India’s TeleMANAS mental health support system, to provide structured AI-human collaboration for long-term care and crisis interventions. It will be an adaptable, scalable, modular, and plug-and-play solution based on the Digital Public Infrastructure principle. Additionally, we intend to incorporate AI-driven geo-spatial crisis mapping to identify suicide hotspots in underserved regions. By combining real-time multimodal risk detection, professional mental health intervention, and geo-spatial outreach, SAHAY represents a scalable, adaptable, and end-to-end suicide prevention system. The design of SAHAY aligns with UN Sustainable Development Goals (SDGs) 3, 4, 5, 10, and 17, promoting inclusive, accessible, and data-driven mental healthcare.

Amey Hengle, Prasoon Bajpai, Soham Dan, Tanmoy Chakraborty. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Amey Hengle, Aswini Kumar Padhi, Anil Bandhakavi, Tanmoy Chakraborty. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Key-value (KV) cache compression has emerged as a critical technique for reducing the memory and latency overhead of autoregressive language models during inference. Prior approaches predominantly rely on query-key attention scores to rank and evict cached tokens, assuming that attention intensity correlates with semantic importance. However, this heuristic overlooks the contribution of value vectors, which directly influence the attention output. In this paper, we propose CurDKV, a novel, value-centric KV compression method that selects keys and values based on leverage scores computed from CUR matrix decomposition. Our approach approximates the dominant subspace of the attention output $\mathrm{softmax}(QK^\top)V$, ensuring that the retained tokens best preserve the model’s predictive behavior. Theoretically, we show that attention score approximation does not guarantee output preservation, and demonstrate that CUR-based selection minimizes end-to-end attention reconstruction loss. Empirically, CurDKV achieves up to $9.6$\% higher accuracy than state-of-the-art methods like SnapKV and ChunkKV under aggressive compression budgets on LLaMA and Mistral, while maintaining compatibility with FlashAttention and Grouped Query Attention. In addition to improved accuracy, CurDKV reduces generation latency by up to 40\% at high compression, offering a practical speed-accuracy tradeoff.

The complexity of scRNA-sequencing datasets highlights the urgent need for enhanced clustering and visualization methods. Here, we propose Stardust, an iterative, force-directed graph layout algorithm that enables the simultaneous embedding of cells and marker genes. Stardust, for the first time, allows a single-stop visualization of cells and marker genes on a single 2D map. While Stardust provides its own visualization pipeline, it can be plugged in with state-of-the-art methods such as Uniform Manifold Approximation and Projection (UMAP) and t-Distributed Stochastic Neighbor Embedding (t-SNE). We benchmarked Stardust against popular visualization and clustering tools on both scRNA-seq and spatial transcriptomics datasets. In all cases, Stardust performs competitively in identifying and visualizing cell types in an accurate and spatially coherent manner.

Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.

Abstract Fine-tuning large language models (LLMs) on downstream tasks requires substantial computational resources. Selective-PEFT, a class of parameter-efficient fine-tuning (PEFT) methodologies, aims to mitigate these computational challenges by selectively fine-tuning only a small fraction of the model parameters. Although parameter-efficient, these techniques often fail to match the performance of fully fine-tuned models, primarily due to inherent biases introduced during parameter selection. Traditional selective-PEFT techniques use a fixed set of parameters selected using different importance heuristics, failing to capture parameter importance dynamically and often leading to suboptimal performance. We introduce ID3, a novel selective-PEFT method that calculates parameter importance continually, and dynamically unmasks parameters by balancing exploration and exploitation in parameter selection. Our empirical study on 16 tasks spanning natural language understanding, mathematical reasoning, and summarization demonstrates the effectiveness of our method compared to fixed-masking selective-PEFT techniques. We analytically show that ID3 reduces the number of gradient updates by a factor of two, enhancing computational efficiency. Since ID3 is robust to random initialization of neurons and operates directly on the optimization process, it is highly flexible and can be integrated with existing additive and reparameterization-based PEFT techniques such as Adapters and LoRA, respectively.1

Large Language Models (LLM) exhibit zero-shot mathematical reasoning capacity as a behavior emergent with scale, commonly manifesting as chain-of-thoughts (CoT) reasoning. However, multiple empirical findings suggest that this prowess is exclusive to LLMs that have exorbitant sizes (beyond 50 billion parameters). Meanwhile, educational neuroscientists suggest that symbolic algebraic manipulation be introduced around the same time as arithmetic word problems so as to modularize language-to-formulation, symbolic manipulation of the formulation, and endgame arithmetic. In this paper, we start with the hypothesis that much smaller LMs, which are weak at multi-step reasoning, can achieve reasonable arithmetic reasoning if arithmetic word problems are posed as a formalize-then-solve task. In our architecture, which we call SyReLM, the LM serves the role of a translator to map natural language arithmetic questions into a formal language (FL) description. A symbolic solver then evaluates the FL expression to obtain the answer. A small frozen LM, equipped with an efficient low-rank adapter, is capable of generating FL expressions that incorporate natural language descriptions of the arithmetic problem (e.g., variable names and their purposes, formal expressions combining variables, etc.). We adopt policy-gradient reinforcement learning to train the adapted LM, informed by the non-differentiable symbolic solver. This marks a sharp departure from the recent development in tool-augmented LLMs, in which the external tools (e.g., calculator, Web search, etc.) are essentially detached from the learning phase of the LM. SyReLM shows massive improvements (e.g., +30.65 absolute point improvement in accuracy on the SVAMP dataset using GPT-J 6B model) over base LMs, while keeping our testbed easy to diagnose and interpret, and within the reach of most researchers.

Large Language Models (LLMs) have transformed NLP with their remarkable In-context Learning (ICL) capabilities.Automated assistants based on LLMs are gaining popularity; however, adapting them to novel tasks is still challenging.While colossal models excel in zero-shot performance, their computational demands limit widespread use, and smaller language models struggle without context.This paper investigates whether LLMs can generalize from labeled examples of predefined tasks to novel tasks.Drawing inspiration from biological neurons and the mechanistic interpretation of the Transformer architecture, we explore the potential for information sharing across tasks.We design a cross-task prompting setup with three LLMs and show that LLMs achieve significant performance improvements despite no examples from the target task in the context.Cross-task prompting leads to a remarkable performance boost of 107% for LLaMA-2 7B, 18.6% for LLaMA-2 13B, and 3.2% for GPT 3.5 on average over zeroshot prompting, and performs comparable to standard in-context learning.The effectiveness of generating pseudo-labels for in-task examples is demonstrated, and our analyses reveal a strong correlation between the effect of crosstask examples and model activation similarities in source and target input tokens.This paper offers a first-of-its-kind exploration of LLMs' ability to solve novel tasks based on contextual signals from different task examples.Definition: You are given a passage as context and a question related to the passage that can be answered as "True" or "False".Based on the context, question and your reasoning ability answer in a "True" and "False".Context: Many organisms, the action potential is actually initially carried.... Question: Do all neurons have the same action potential?Answer: False Context: Intersex is in some caused by unusual sex hormones.... Question: Can u be born with both male and female parts?Answer: True Context: The Gregorian leap cycle, which has 97 leap days spread.... Question: Are there ever 53 weeks in a year?Answer: True Definition: Given a question from a scientific exam about Physics, Chemistry, and Biology, among others.The question is in multiple choice format with four answer options A., B., C. and D. Using your knowledge about the scientific fields answer the question and provide the label A, B, C and D as answer.Question: Which of the following is not true for myelinated nerve fibers:A. Impulse through ........ neural fibers B. Membrane ...... of Ranvier C. Saltatory .... is seen D. Local anesthesia ....... when the nerve is not covered by myelin sheath Semantically similar example selectionDefinition: You are given a passage as context and a question related to the passage that can be answered as "True" or "False"......

The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This has resulted a formidable challenge for the computational models due to the scarcity of annotated data and presence of noise. A potential solution to mitigate the data scarcity problem in low-resource setup is to leverage existing data in resource-rich language through translation. In this paper, we tackle the problem of code-mixed (Hinglish and Bengalish) to English machine translation. First, we synthetically develop HINMIX, a parallel corpus of Hinglish to English, with ~4.2M sentence pairs. Subsequently, we propose RCMT, a robust perturbation based joint-training model that learns to handle noise in the real-world code-mixed text by parameter sharing across clean and noisy words. Further, we show the adaptability of RCMT in a zero-shot setup for Bengalish to English translation. Our evaluation and comprehensive analyses qualitatively and quantitatively demonstrate the superiority of RCMT over state-of-the-art code-mixed and robust translation methods.

In the realm of conversational dynamics, individual idiosyncrasies challenge the suitability of a one-size-fits-all approach for dialogue agent responses. Prior studies often assumed the speaker’s persona’s immediate availability, a premise not universally applicable. To address this gap, we explore the Speaker Profiling in Conversations (SPC) task, aiming to synthesize persona attributes for each dialogue participant. SPC comprises three core subtasks: persona discovery, persona-type identification, and persona-value extraction. The first subtask identifies persona-related utterances, the second classifies specific attributes, and the third extracts precise values for the persona. To confront this multifaceted challenge, we’ve diligently compiled SPICE, an annotated dataset, underpinning our thorough evaluation of diverse baseline models. Additionally, we benchmark these findings against our innovative neural model, SPOT, presenting an exhaustive analysis encompassing a nuanced assessment of quantitative and qualitative merits and limitations.

Large Language Models (LLMs) and AI assistants driven by these models are experiencing exponential growth in usage among both expert and amateur users.In this work, we focus on evaluating the reliability of current LLMs as science communicators.Unlike existing benchmarks, our approach emphasizes assessing these models on scientific questionanswering tasks that require a nuanced understanding and awareness of answerability.We introduce a novel dataset, SCiPS-QA, comprising 742 Yes/No queries embedded in complex scientific concepts, along with a benchmarking suite that evaluates LLMs for correctness and consistency across various criteria.We benchmark three proprietary LLMs from the OpenAI GPT family and 13 open-access LLMs from the Meta Llama-2, Llama-3, and Mistral families.While most open-access models significantly underperform compared to GPT-4 Turbo, our experiments identify Llama-3-70B as a strong competitor, often surpassing GPT-4 Turbo in various evaluation aspects.We also find that even the GPT models exhibit a general incompetence in reliably verifying LLM responses.Moreover, we observe an alarming trend where human evaluators are deceived by incorrect responses from GPT-4 Turbo.

The prolific use of Large Language Models (LLMs) as an alternate knowledge base requires them to be factually consistent, necessitating both correctness and consistency traits for paraphrased queries.Recently, significant attempts have been made to benchmark datasets and metrics to evaluate LLMs for these traits.However, structural simplicity (subject-relation-object) and contemporary association in their query formulation limit the broader definition of factuality and consistency.In this study, we introduce TeCFaP, a novel Temporally Consistent Factuality Probe task to expand the consistent factuality probe in the temporal dimension.To this end, we propose TEMP-COFAC, a high-quality dataset of prefixstyle English query paraphrases.Subsequently, we extend the definitions of existing metrics to represent consistent factuality across temporal dimension.We experiment with a diverse set of LLMs and find most of them performing poorly on TeCFaP.Next, we propose a novel solution CoTSeLF (Consistent-Time-Sensitive Learning Framework) combining multi-task instruction tuning (MT-IT) with consistent-time-sensitive reinforcement learning (CTSRL) to improve temporally consistent factuality in LLMs.Our experiments demonstrate the efficacy of CoTSeLF over several baselines.* We leverage GPT-4 to extend manually constructed initial set of base subject-relation pairs.* All of them are male with their ages ranging between 25-35 years.

Despite demonstrating emergent reasoning abilities, Large Language Models (LLMS) often lose track of complex, multi-step reasoning.Existing studies show that providing guidance via decomposing the original question into multiple subproblems elicits more robustness in LLM reasoning -a decomposer generates the subproblems, and a solver solves each of these subproblems.However, these techniques fail to accommodate coordination between the decomposer and the solver modules (either in a single model or different specialized ones) -the decomposer does not keep track of the ability of the solver to follow the decomposed reasoning.In this paper, we propose LM 2 to address these challenges.LM 2 modularizes the decomposition, solution, and verification into three different language models.The decomposer module identifies the key concepts necessary to solve the problem and generates step-by-step subquestions according to the reasoning requirement.The solver model generates the solution to the subproblems that are then checked by the verifier module; depending upon the feedback from the verifier, the reasoning context is constructed using the subproblems and the solutions.These models are trained to coordinate using policy learning.Exhaustive experimentation suggests the superiority of LM 2 over existing methods on in-and out-domain reasoning problems, outperforming the best baselines by 8.1% on MATH, 7.71% on JEEBench, and 9.7% on MedQA problems (code available at https://github.com/ LCS2- IIITD/Language_Model_Multiplex).

For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear.By including additional context in prompts, we comprehensively analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected.Our findings on two LLMs, five languages, and six datasets reveal that mimicking persona-based attributes leads to annotation variability.Meanwhile, incorporating geographical signals leads to better regional alignment.We also find that the LLMs are sensitive to numerical anchors, indicating the ability to leverage community-based flagging efforts and exposure to adversaries.Our work provides preliminary guidelines and highlights the nuances of applying LLMs in culturally sensitive cases. 1

Large Language Models (LLMs) have succeeded considerably in In-Context-Learning (ICL) based summarization.However, saliency is subject to the users' specific preference histories.Hence, we need reliable In-Context Personalization Learning (ICPL) capabilities within such LLMs.For any arbitrary LLM to exhibit ICPL, it needs to have the ability to discern contrast in user profiles.A recent study proposed a measure for degree-ofpersonalization called EGISES for the first time.EGISES measures a model's responsiveness to user profile differences.However, it cannot test if a model utilizes all three types of cues provided in ICPL prompts: (i) example summaries, (ii) user's reading histories, and (iii) contrast in user profiles.To address this, we propose the iCOPERNICUS framework, a novel In-Context Personalization Learning Scrutiny of Summarization capability in LLMs that uses EGISES as a comparative measure.As a casestudy, we evaluate 17 state-of-the-art LLMs based on their reported ICL performances and observe that 15 models' ICPL degrades (min: 1.6%↓; max: 3.6%↓) when probed with richer prompts, thereby showing lack of true ICPL.

In mental health counseling, condensing dialogues into concise and relevant summaries (aka counseling notes) holds pivotal significance.Large Language Models (LLMs) exhibit remarkable capabilities in various generative tasks; however, their adaptation to domainspecific intricacies remains challenging, especially within mental health contexts.Unlike standard LLMs, mental health experts first plan to apply domain knowledge in writing summaries.Our work enhances LLMs' ability by introducing a novel planning engine to orchestrate structuring knowledge alignment.To achieve high-order planning, we divide knowledge encapsulation into two major phases: (i) holding dialogue structure and (ii) incorporating domain-specific knowledge.We employ a planning engine on Llama-2, resulting in a novel framework, PIECE.Our proposed system employs knowledge filtering-cum-scaffolding to encapsulate domain knowledge.Additionally, PIECE leverages sheaf convolution learning to enhance its understanding of the dialogue's structural nuances.We compare PIECE with 14 baseline methods and observe a significant improvement across ROUGE and Bleurt scores.Further, expert evaluation and analyses validate the generation quality to be effective, sometimes even surpassing the gold standard.We further benchmark PIECE with other LLMs and report improvement, including Llama-2 (+2.72%),Mistral (+2.04%) and Zephyr (+1.59%), to justify the generalizability of the planning engine.Counseling Dialogue C: I'm done talking to all of you; it's a waste of time.You're all the same, offering nothing.T: You're saying we're all alike?C: Yes, everyone's the same, no solutions.How do I get better?I came here to fix things. T: Do you need a step-by-step plan?C: Yes, something tangible to grasp.T: It needs an organic process between us. C: I'm always intense

Knowledge distillation (KD) is a technique used to transfer knowledge from a larger ''teacher'' model into a smaller ''student'' model. Recent advancements in meta-learning-based knowledge distillation (MetaKD) emphasize that the fine-tuning of teacher models should be aware of the student's need to achieve better knowledge distillation. However, existing MetaKD methods often lack incentives for the teacher model to improve itself. In this study, we introduce MPDistil, a meta-policy distillation technique, that utilizes novel optimization strategies to foster both *collaboration* and *competition* during the fine-tuning of the teacher model in the meta-learning step. Additionally, we propose a curriculum learning framework for the student model in a competitive setup, in which the student model aims to outperform the teacher model by self-training on various tasks. Exhaustive experiments on SuperGLUE and GLUE benchmarks demonstrate the efficacy of MPDistil compared to $20$ conventional KD and advanced MetaKD baselines, showing significant performance enhancements in the student model -- e.g., a distilled 6-layer BERT model outperforms a 12-layer BERT model on five out of six SuperGLUE tasks. Furthermore, MPDistil, while applied to a large language teacher model (DeBERTa-v2-xxlarge), significantly narrows the performance gap of its smaller student counterpart (DeBERTa-12) by just $4.6$% on SuperGLUE. We further demonstrate how higher rewards and customized training curricula strengthen the student model and enhance generalizability.

Message passing on hypergraphs has been a standard framework for learning higher-order correlations between hypernodes.Recentlyproposed hypergraph neural networks (HGNNs) can be categorized into spatial and spectral methods based on their design choices.In this work, we analyze the impact of change in hypergraph topology on the suboptimal performance of HGNNs and propose DPHGNN, a novel dual-perspective HGNN that introduces equivariant operator learning to capture lower-order semantics by inducing topologyaware spatial and spectral inductive biases.DPHGNN employs a unified framework to dynamically fuse lower-order explicit feature representations from the underlying graph into the super-imposed hypergraph structure.We benchmark DPHGNN over eight benchmark hypergraph datasets for the semi-supervised hypernode classification task and obtain superior performance compared to seven state-of-the-art baselines.We also provide a theoretical framework and a synthetic hypergraph isomorphism test to express the power of spatial HGNNs and quantify the expressivity of DPHGNN beyond the Generalized Weisfeiler Leman (1-GWL) test.Finally, DPHGNN was deployed by our partner e-commerce company, Meesho for the Return-to-Origin (RTO) prediction task, which shows 7% higher macro F1-Score than the best baseline.

Amey Hengle, Aswini Padhi, Sahajpreet Singh, Anil Bandhakavi, Md Shad Akhtar, Tanmoy Chakraborty. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

MOTIVATION: Dysregulation of a gene's function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene-pathology relationships is an ever-daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape project, researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite diverse and is confounded by several clinical and demographic covariates. RESULTS: To circumvent this, we mined ∼18 million PubMed abstracts published till May 2019 and automatically selected ∼4.5 million of them that describe roles of particular genes in disease pathogenesis. Further, we fine-tuned the pretrained bidirectional encoder representations from transformers (BERT) for language modeling from the domain of natural language processing to learn vector representation of entities such as genes, diseases, tissues, cell-types, etc., in a way such that their relationship is preserved in a vector space. The repurposed BERT predicted disease-gene associations that are not cited in the training data, thereby highlighting the feasibility of in silico synthesis of hypotheses linking different biological entities such as genes and conditions. AVAILABILITY AND IMPLEMENTATION: PathoBERT pretrained model: https://github.com/Priyadarshini-Rai/Pathomap-Model. BioSentVec-based abstract classification model: https://github.com/Priyadarshini-Rai/Pathomap-Model. Pathomap R package: https://github.com/Priyadarshini-Rai/Pathomap.

Summary Detecting an accurate community structure is a crucial task in network analysis. With the increasing popularity of social networking sites, it is essential to have a community detection algorithm that is not only efficien but also cost‐effective for running in data centers. There are several metrics for estimating the accuracy of community detection. However, previous research has shown that permanence, a vertex‐centric metric, provides the most precise estimate of a community structure compared to other approaches. Despite this, no study has been conducted on parallelizing a permanence‐based community detection algorithm and analyzing its energy efficiency. This article introduces Amoeba, a task parallel implementation of a permanence‐based community detection algorithm designed for multicore processors. It uses dynamic tasking to schedule the inherent irregular computation, and it can dynamically adapt the total number of parallel threads, which results in improved energy efficiency. We evaluated Amoeba using several real‐world and artificial graphs on a multicore server processor. Our experimental results show that Amoeba achieves a geometric mean speedup of 15.3 over its sequential implementation, and due to thread adaptability, it achieves energy savings of 12.4% and a speedup of 6% over its nonadaptive implementation.

The ever-evolving social media discourse has witnessed an overwhelming use of memes to express opinions or dissent. Besides being misused for spreading malcontent, they are mined by corporations and political parties to glean the public's opinion. Therefore, memes predominantly offer affect-enriched insights towards ascertaining the societal psyche. However, the current approaches are yet to model the affective dimensions expressed in memes effectively. They rely extensively on large multimodal datasets for pre-training and do not generalize well due to constrained visual-linguistic grounding. In this paper, we introduce MOOD (Meme emOtiOns Dataset), which embodies six basic emotions. We then present ALFRED (emotion-Aware muLtimodal Fusion foR Emotion Detection), a novel multimodal neural framework that (i) explicitly models emotion-enriched visual cues, and (ii) employs an efficient cross-modal fusion via a gating mechanism. Our investigation establishes ALFRED's superiority over existing baselines by 4.94% F1. Additionally, ALFRED competes strongly with previous best approaches on the challenging Memotion task. We then discuss ALFRED's domain-agnostic generalizability by demonstrating its dominance on two recently-released datasets - HarMeme and Dank Memes, over other baselines. Further, we analyze ALFRED's interpretability using attention maps. Finally, we highlight the inherent challenges posed by the complex interplay of disparate modality-specific cues toward meme analysis.

Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we proposeHateMAML, a model-agnostic meta-learning (MAML)-based framework that effectively performs hate speech detection in low-resource languages.HateMAMLutilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show thatHateMAMLoutperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics ofHateMAML.

In this article, we deal with the task of hostile post detection in Hindi. The objective is to predict whether a social media post is hostile or not. Furthermore, if the post is hostile, we identify one or more fine-grained hostile dimensions out of the following four—fake, hate, offensive, and defamation. We proposeHostileNet, a novel deep-learning framework that leverages HindiBERT-based contextual representations and hand-crafted features like lexicon, emoticon, and hashtag embeddings for hostile post classification. Moreover, we also propose a novel mechanism to fine-tune HindiBERT’s attention vectors with respect to each hostile dimension. We evaluateHostileNeton the CONSTRAINT-2021 shared task dataset on hostile post detection in Hindi for both coarse-grained (hostile versus nonhostile) and fine-grained (fake versus hate versus offensive versus defamation) setups.HostileNetoutperforms the best-performing system as reported in the CONSTRAINT-2021 shared task for both the setups. Furthermore, we provide a thorough analysis of the obtained results in the form of an ablation study, error analysis, attention heatmap analysis, lexicon feature analysis, and so on. We also perform in-the-wild evaluation and conduct a user survey to assess the robustness of our proposed model. We make the code and the curated multilabel hostile lexicon available for research use athttps://github.com/LCS2-IIITD/HostileNet.

Being a popular mode of text-based communication in multilingual communities, code mixing in online social media has become an important subject to study. Learning the semantics and morphology of code-mixed language remains a key challenge due to the scarcity of data, the unavailability of robust, and language-invariant representation learning techniques. Any morphologically rich language can benefit from character, subword, and word-level embeddings, aiding in learning meaningful correlations. In this article, we explore a hierarchical transformer (HIT)-based architecture to learn the semantics of code-mixed languages. HIT consists of multiheaded self-attention (MSA) and outer product attention components to simultaneously comprehend the semantic and syntactic structures of code-mixed texts. We evaluate the proposed method across six Indian languages (Bengali, Gujarati, Hindi, Tamil, Telugu, and Malayalam) and Spanish for nine tasks on 17 datasets. The HIT model outperforms state-of-the-art code-mixed representation learning and multilingual language models on 13 datasets across eight tasks. We further demonstrate the generalizability of the HIT architecture using masked language modeling (MLM)-based pretraining, zero-shot learning (ZSL), and transfer learning approaches. Our empirical results show that the pretraining objectives significantly improve the performance of downstream tasks.

Conversations emerge as the primary media for exchanging ideas and conceptions. From the listener’s perspective, identifying various affective qualities, such as sarcasm, humour, and emotions, is paramount for comprehending the true connotation of the emitted utterance. However, one of the major hurdles faced in learning these affect dimensions is the presence of figurative language, viz. irony, metaphor, or sarcasm. We hypothesize that any detection system constituting the exhaustive and explicit presentation of the emitted utterance would improve the overall comprehension of the dialogue. To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances. We propose MOSES, a deep neural network which takes a multimodal (sarcastic) dialogue instance as an input and generates a natural language sentence as its explanation. Subsequently, we leverage the generated explanation for various natural language understanding tasks in a conversational dialogue setup, such as sarcasm detection, humour identification, and emotion recognition. Our evaluation shows that MOSES outperforms the state-of-the-art system for SED by an average of ∼2% on different evaluation metrics, such as ROUGE, BLEU, and METEOR. Further, we observe that leveraging the generated explanation advances three downstream tasks for affect classification – an average improvement of ~14% F1-score in the sarcasm detection task and ∼2% in the humour identification and emotion recognition task. We also perform extensive analyses to assess the quality of the results.

Memes are powerful means for effective communication on social media. Their effortless amalgamation of viral visuals and compelling messages can have far-reaching implications with proper marketing. Previous research on memes has primarily focused on characterizing their affective spectrum and detecting whether the meme's message insinuates any intended harm, such as hate, offense, racism, etc. However, memes often use abstraction, which can be elusive. Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes. To this end, we curate ExHVV, a novel dataset that offers natural language explanations of connotative roles for three types of entities - heroes, villains, and victims, encompassing 4,680 entities present in 3K memes. We also benchmark ExHVV with several strong unimodal and multimodal baselines. Moreover, we posit LUMEN, a novel multimodal, multi-task learning framework that endeavors to address EXCLAIM optimally by jointly learning to predict the correct semantic roles and correspondingly to generate suitable natural language explanations. LUMEN distinctly outperforms the best baseline across 18 standard natural language generation evaluation metrics. Our systematic evaluation and analyses demonstrate that characteristic multimodal cues required for adjudicating semantic roles are also helpful for generating suitable explanations.

Rishabh Gupta, Shaily Desai, Manvi Goel, Anil Bandhakavi, Tanmoy Chakraborty, Md. Shad Akhtar. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Memes are a powerful tool for communication over social media.Their affinity for evolving across politics, history, and sociocultural phenomena makes them an ideal communication vehicle.To comprehend the subtle message conveyed within a meme, one must understand the background that facilitates its holistic assimilation.Besides digital archiving of memes and their metadata by a few websites like knowyourmeme.com,currently, there is no efficient way to deduce a meme's context dynamically.In this work, we propose a novel task, MEMEX -given a meme and a related document, the aim is to mine the context that succinctly explains the background of the meme.At first, we develop MCC (Meme Context Corpus), a novel dataset for MEMEX.Further, to benchmark MCC, we propose MIME (MultImodal Meme Explainer), a multimodal neural framework that uses common sense enriched meme representation and a layered approach to capture the cross-modal semantic dependencies between the meme and the context.MIME surpasses several unimodal and multimodal systems and yields an absolute improvement of ⇡ 4% F1-score over the best baseline.Lastly, we conduct detailed analyses of MIME's performance, highlighting the aspects that could lead to optimal modeling of cross-modal contextual associations.

In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update.ICL-enabled large language models provide a promising step forward toward bypassing recurrent annotation costs in a low-resource setting.Yet, only a handful of past studies have explored ICL in a cross-lingual setting, in which the need for transferring label-knowledge from a high-resource language to a low-resource one is immensely crucial.To bridge the gap, we provide the first in-depth analysis of ICL for cross-lingual text classification.We find that the prevalent mode of selecting random inputlabel pairs to construct the prompt-context is severely limited in the case of cross-lingual ICL, primarily due to the lack of alignment in the input as well as the output spaces.To mitigate this, we propose a novel prompt construction strategy -Cross-lingual In-context Source-Target Alignment (X-InSTA).With an injected coherence in the semantics of the input examples and a task-based alignment across the source and target languages, X-InSTA is able to outperform random prompt selection by a large margin across three different tasks using 44 different cross-lingual pairs.

Shivam Sharma, Atharva Kulkarni, Tharun Suresh, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, Tanmoy Chakraborty. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Multi-document Summarization (MDS) characterizes compressing information from multiple source documents to its succinct summary.An ideal summary should encompass all topics and accurately model cross-document relations expounded upon in the source documents.However, existing systems either impose constraints on the length of tokens during the encoding or falter in capturing the intricate cross-document relationships.These limitations impel the systems to produce summaries that are non-factual and unfaithful, thereby imparting an unfair comprehension of the topic to the readers.To counter these limitations and promote the information equivalence between the source document and generated summary, we propose FABRIC, a novel encoder-decoder model that uses pre-trained BART to comprehensively analyze linguistic nuances, simplicial complex layer to apprehend inherent properties that transcend pairwise associations and sheaf graph attention to effectively capture the heterophilic properties.We benchmark FABRIC with eleven baselines over four widely-used MDS datasets -Multinews, CQASumm, DUC and Opinosis, and show that FABRIC achieves consistent performance improvement across all the evaluation metrics (syntactical, semantical and faithfulness).We corroborate these improvements further through qualitative human evaluation.

Large Language Models (LLMs) prompted to generate chain-of-thought (CoT) exhibit impressive reasoning capabilities.Recent attempts at prompt decomposition toward solving complex, multi-step reasoning problems depend on the ability of the LLM to simultaneously decompose and solve the problem.A significant disadvantage is that foundational LLMs are typically not available for finetuning, making adaptation computationally prohibitive.We believe (and demonstrate) that problem decomposition and solution generation are distinct capabilites, better addressed in separate modules, than by one monolithic LLM.We introduce DaSLaM, which uses a decomposition generator to decompose complex problems into subproblems that require fewer reasoning steps.These subproblems are answered by a solver.We use a relatively small (13B parameters) LM as the decomposition generator, which we train using policy gradient optimization to interact with a solver LM (regarded as blackbox) and guide it through subproblems, thereby rendering our method solver-agnostic.Evaluation on multiple different reasoning datasets reveal that with our method, a 175 billion parameter LM (text-davinci-003) can produce competitive or even better performance, compared to its orders-of-magnitude larger successor, GPT-4.Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e.g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.Exhaustive ablation studies evince the superiority of our modular finetuning technique over exorbitantly large decomposer LLMs, based on prompting alone.

Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC).While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional dynamics in codemixed conversations has received relatively less attention.This motivates our undertaking of ERC for code-mixed conversations in this study.Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.To achieve this, we devise an efficient pipeline that extracts relevant commonsense from existing knowledge graphs based on the code-mixed input.Subsequently, we develop an advanced fusion technique that seamlessly combines the acquired commonsense information with the dialogue representation obtained from a dedicated dialogue understanding module.Our comprehensive experimentation showcases the substantial performance improvement obtained through the systematic incorporation of commonsense in ERC.Both quantitative assessments and qualitative analyses further corroborate the validity of our hypothesis, reaffirming the pivotal role of commonsense integration in enhancing ERC.

Poverty is a multifaceted phenomenon linked to the lack of capabilities of households to earn a sustainable livelihood, increasingly being assessed using multidimensional indicators. Its spatial pattern depends on social, economic, political, and regional variables. Artificial intelligence has shown immense scope in analyzing the complexities and nuances of poverty. The proposed project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators. The districts will be classified into ‘advanced’, ‘catching up’, ‘falling behind’, and ‘lagged’ regions. The project proposes to integrate multiple data sources, including conventional national-level large sample household surveys, census surveys, and proxy variables like daytime, and nighttime data from satellite images, and communication networks, to name a few, to provide a comprehensive view of poverty at the district level. The project also intends to examine causation and longitudinal analysis to examine the reasons for poverty. Poverty and inequality could be widening in developing countries due to demographic and growth-agglomerating policies. Therefore, targeting the lagging regions and the vulnerable population is essential to eradicate poverty and improve the quality of life to achieve the goal of ‘zero poverty’. Thus, the study also focuses on the districts with a higher share of the marginal section of the population compared to the national average to trace the performance of development indicators and their association with poverty in these regions.

The realm of scientific text summarization has experienced remarkable progress due to the availability of annotated brief summaries and ample data. However, the utilization of multiple input modalities, such as videos and audio, has yet to be thoroughly explored. At present, scientific multimodal-input-based text summarization systems tend to employ longer target summaries like abstracts, leading to an underwhelming performance in the task of text summarization.

Social media is awash with hateful content, much of which is often veiled with linguistic and topical diversity. The benchmark datasets used for hate speech detection do not account for such divagation as they are predominantly compiled using hate lexicons. However, capturing hate signals becomes challenging in neutrally-seeded malicious content. Thus, designing models and datasets that mimic the real-world variability of hate warrants further investigation.

Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space. An assistive counseling conversation commences with natural open-ended topics to familiarize the client with the environment and later converges into more fine-grained domain-specific topics. Unlike other conversational systems, which are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid conversational flow. These counseling bots need to comprehend various aspects of the conversation, such as dialogue-acts, intents, etc., to engage the client in an effective and appropriate conversation. Although the surge in digital health research highlights applications of many general-purpose response generation systems, they are barely suitable in the mental health domain – the prime reason is the lack of understanding in the mental health counseling conversation. Moreover, in general, dialogue-act guided response generators are either limited to a template-based paradigm or lack appropriate semantics in dialogue generation. To this end, we propose READER – a REsponse-Act guided reinforced Dialogue genERation model for the mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act dt + 1 for the next utterance (aka response-act) and to generate an appropriate response (ut + 1). Through the transformer-reinforcement-learning (TRL) with Proximal Policy Optimization (PPO), we guide the response generator to abide by dt + 1 and ensure the semantic richness of the responses via BERTScore in our reward computation. We evaluate READER on HOPE, a benchmark counseling conversation dataset and observe that it outperforms several baselines across several evaluation metrics – METEOR, ROUGE, and BERTScore.

Although in the last decade several fact-checking organizations have emerged to verify misinformation, fake news has continued to proliferate, especially through social media platforms. Even though adopting improved detection strategies is of utmost importance, the fact-checking process could be optimised by verifying whether a claim has been previously fact-checked. Despite some ad-hoc information retrieval approaches having been recently proposed, the utility of modern (neural) retrieval systems have not been investigated yet. In this paper, we consider the standard two-phases retriever-reranker architecture and benchmark different state-of-the-art techniques from the information retrieval and Q&A literature. We design several experiments on a real-world Twitter dataset to analyse the efficiency and the effectiveness of the benchmark approaches. Our results show that combining standard and neural approaches is the most promising research direction to improve retrievers performance and that complex (neural) rerankers might still be efficient in practice since there is no need to process a high number of documents to improve ranking performance.

Sarcasm detection and humor classification are inherently subtle problems, primarily due to their dependence on the contextual and non-verbal information. Furthermore, existing studies in these two topics are usually constrained in non-English languages such as Hindi, due to the unavailability of qualitative annotated datasets. In this work, we make two major contributions considering the above limitations: (1) we develop a Hindi-English code-mixed dataset,MaSaC,1for the multi-modal sarcasm detection and humor classification in conversational dialog, which to our knowledge is the first dataset of its kind; (2) we proposeMSH-COMICS,2a novel attention-rich neural architecture for the utterance classification. We learn efficient utterance representation utilizing a hierarchical attention mechanism that attends to a small portion of the input sentence at a time. Further, we incorporate dialog-level contextual attention mechanism to leverage the dialog history for the multi-modal classification. We perform extensive experiments for both the tasks by varying multi-modal inputs and various submodules ofMSH-COMICS. We also conduct comparative analysis against existing approaches. We observe thatMSH-COMICSattains superior performance over the existing models by$>$1 F1-score point for the sarcasm detection and 10 F1-score points in humor classification. We diagnose our model and perform thorough analysis of the results to understand the superiority and pitfalls.

Abstractive text summarization systems using recently improved RNN-based sequence-to-sequence architecture have shown great promise for single-document summarization. However, such neural models fail to perpetuate the performance in the multi-document summarization setting owing to the long-range dependencies within the documents, overlapping/contradicting facts and extrinsic model hallucinations. These shortcomings augment the model to generate inconsistent, repetitive and non-factual summaries. In this work, we introduce REISA, a sequence-to-sequence model with a novelreinforced selective attention spanthat attends over the input and recalibrates the local attention weights to focus on important segments while generating output at each time step. REISA utilizes a reinforcement learning-based policy gradient algorithm to reward the model and formulate attention distributions over the encoder input. We further benchmark REISA on two widely-used multi-document summarization corpora – Multinews and CQASumm, and observe an improvement of$+2.91$and$+6.64$ROUGE-L scores, respectively. The qualitative analyses on semantic similarity by BERTScore, faithfulness by question-answer evaluation and human evaluation show significant improvement over the baseline-generated summaries.

With an increasing outreach of digital platforms in our lives, researchers have taken a keen interest in studying different facets of social interactions. Analyzing the spread of information ( aka diffusion) has brought forth multiple research areas such as modelling user engagement, determining emerging topics, forecasting the virality of online posts and predicting information cascades. Despite such ever-increasing interest, there remains a vacuum among easy-to-use interfaces for large-scale visualization of diffusion models. In this article, we introduce DiVA — Di ffusion V isualization and A nalysis, a tool that provides a scalable web interface and extendable APIs to analyze various diffusion trends on networks. DiVA uniquely offers support for simultaneous comparison of two competing diffusion models and even the comparison with the ground-truth results, which help develop a coherent understanding of real-world scenarios. Along with performing an exhaustive feature comparison and system evaluation of DiVA against publicly-available web interfaces for information diffusion, we conducted a user study to understand the strengths and limitations of DiVA . We noticed that evaluators had a seamless user experience, especially when analyzing diffusion on large networks.

Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods (such as deepwalk,node2vec,LINE) are based on a neural architecture, thus unable to scale on large networks both in terms of time and space usage. Recently, we proposed BinSketch, a sketching technique for compressing binary vectors to binary vectors. In this paper, we show how to extend BinSketch and use it for network hashing. Our proposal named QUINT is built upon BinSketch, and it embeds nodes of a sparse network onto a low-dimensional space using simple bit-wise operations. QUINT is the first of its kind that provides tremendous gain in terms of speed and space usage without compromising much on the accuracy of the downstream tasks. Extensive experiments are conducted to compare QUINT with seven state-of-the-art network embedding methods for two end tasks link prediction and node classification. We observe huge performance gain for QUINT in terms of speedup (up to 7000) and space saving (up to 800) due to its bit-wise nature to obtain node embedding.Moreover, QUINT is a consistent top-performer for both the tasks among the baselines across all the datasets. Our empirical observations are backed by rigorous theoretical analysis to justify the effectiveness of QUINT.

The behavior of information cascades (such as retweets) has been modeled extensively. While point process-based generative models have long been in use for estimating cascade growths, deep learning has greatly enhanced the integration of diverse features and signals. We observe two significant temporal signals in cascade data that have not been reported or exploited to our knowledge. First, the popularity of the cascade root is known to influence cascade size strongly; but we find that the effect falls off rapidly with time. Second, we find a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade. Responding to these observations, we proposeGammaCas, a new cascade growth model as a parametric function of time, which combines deep influence signals from content (e.g., tweet text), network features (e.g., followers of the root user), and exogenous event sources (e.g., online news). Specifically, our model processes these signals through a customized recurrent network, whose states then provide the parameters of the cascade rate function, which is integrated over time to predict the cascade size. The network parameters are trained end-to-end using observed cascades.GammaCasoutperforms seven recent and diverse baselines significantly on a large-scale dataset of retweet cascades coupled with time-aligned online news — it beats the best baseline with 18.98% increase in terms of Kendall's$\tau$correlation and a reduction of 19.2 in Mean Absolute Percentage Error. Extensive ablation and case studies unearth interesting insights regarding retweet cascade dynamics.

Cohesive subgraph discovery is an important problem in bipartite graph mining. In this paper, we focus on one kind of cohesive structure, called k-biplex, where each vertex of one side is disconnected from at most k vertices of the other side. We consider the large maximal k-biplex enumeration problem which is to list all those maximal k-biplexes with the number of vertices at each side at least a non-negative integer . This formulation, we observe, has various applications and targets to find non-redundant results by excluding non-maximal ones. Existing approaches suffer from massive redundant computations and can only run on small and moderate datasets. Towards improving scalability, we propose an efficient tree-based algorithm with two advanced strategies and powerful pruning techniques. Experimental results on real and synthetic datasets show the superiority of our algorithm over existing approaches.

Sarcasm is a pervading linguistic phenomenon and highly challenging to explain due to its subjectivity, lack of context and deeply-felt opinion. In the multimodal setup, sarcasm is conveyed through the incongruity between the text and visual entities. Although recent approaches deal with sarcasm as a classification problem, it is unclear why an online post is identified as sarcastic. Without proper explanation, end users may not be able to perceive the underlying sense of irony. In this paper, we propose a novel problem -- Multimodal Sarcasm Explanation (MuSE) -- given a multimodal sarcastic post containing an image and a caption, we aim to generate a natural language explanation to reveal the intended sarcasm. To this end, we develop MORE, a new dataset with explanation of 3510 sarcastic multimodal posts. Each explanation is a natural language (English) sentence describing the hidden irony. We benchmark MORE by employing a multimodal Transformer-based architecture. It incorporates a cross-modal attention in the Transformer's encoder which attends to the distinguishing features between the two modalities. Subsequently, a BART-based auto-regressive decoder is used as the generator. Empirical results demonstrate convincing results over various baselines (adopted for MuSE) across five evaluation metrics. We also conduct human evaluation on predictions and obtain Fleiss' Kappa score of 0.4 as a fair agreement among 25 evaluators.

Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining.The intrinsic complexity of these tasks demands powerful learning models.While pretrained Transformerbased Language Models (LM) have been shown to provide state-of-the-art results over different NLP tasks, the scarcity of manually annotated data and the highly domaindependent nature of argumentation restrict the capabilities of such models.In this work, we propose a novel transfer learning strategy to overcome these challenges.We utilize argumentation-rich social discussions from the ChangeMyView subreddit as a source of unsupervised, argumentative discourse-aware knowledge by finetuning pretrained LMs on a selectively masked language modeling task.Furthermore, we introduce a novel promptbased strategy for inter-component relation prediction that compliments our proposed finetuning method while leveraging on the discourse context.Exhaustive experiments show the generalization capability of our method on these two tasks over within-domain as well as out-of-domain datasets, outperforming several existing and employed strong baselines.1

Indirect speech such as sarcasm achieves a constellation of discourse goals in human communication.While the indirectness of figurative language warrants speakers to achieve certain pragmatic goals, it is challenging for AI agents to comprehend such idiosyncrasies of human communication.Though sarcasm identification has been a well-explored topic in dialogue analysis, for conversational systems to truly grasp a conversation's innate meaning and generate appropriate responses, simply detecting sarcasm is not enough; it is vital to explain its underlying sarcastic connotation to capture its true essence.In this work, we study the discourse structure of sarcastic conversations and propose a novel task -Sarcasm Explanation in Dialogue (SED).Set in a multimodal and code-mixed setting, the task aims to generate natural language explanations of satirical conversations.To this end, we curate WITS, a new dataset to support our task.We propose MAF (Modality Aware Fusion), a multimodal context-aware attention and global information fusion module to capture multimodality and use it to benchmark WITS.The proposed attention module surpasses the traditional multimodal fusion baselines and reports the best performance on almost all metrics.Lastly, we carry out detailed analyses both quantitatively and qualitatively.

Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused on factuality, others investigated how harmful the content is. While these two components in the definition of disinformation – (i) factuality, and (ii) harmfulness –, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions.

The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news.The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation.However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength.Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in a post.In this work, we introduce the novel task of Claim Span Identification (CSI).We propose CURT, a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets.Furthermore, along with the standard token classification baselines, we benchmark our dataset with DABERTa, an adapterbased variation of RoBERTa.The experimental results attest that DABERTa outperforms the baseline systems across several evaluation metrics, improving by about 1.5 points.We also report detailed error analysis to validate the model's performance along with the ablation studies.Lastly, we release our comprehensive span annotation guidelines for public use.

Cohesive subgraph discovery is an important problem in bipartite graph mining. In this paper, we focus on one kind of cohesive structure, called$k$-biplex, where each vertex of one side is disconnected from at most$k$vertices of the other side. We consider the large maximal$k$-biplex enumeration problem which is to list all those maximal$k$-biplexes with the number of vertices at each side at least a non-negative integer$\theta$. This formulation aims to find non-redundant results by excluding non-maximal ones and has various applications. Existing approaches suffer from massive redundant computations and can only run on small and moderate datasets. Towards improving scalability, we propose an efficient tree-based algorithm with two advanced strategies and powerful pruning techniques. Experimental results show the superiority of our algorithm over existing approaches.

The automatic identification of harmful content online is of major concern for social media platforms, policymakers, and society. Researchers have studied textual, visual, and audio content, but typically in isolation. Yet, harmful content often combines multiple modalities, as in the case of memes. With this in mind, here we offer a comprehensive survey with a focus on harmful memes. Based on a systematic analysis of recent literature, we first propose a new typology of harmful memes, and then we highlight and summarize the relevant state of the art. One interesting finding is that many types of harmful memes are not really studied, e.g., such featuring self-harm and extremism, partly due to the lack of suitable datasets. We further find that existing datasets mostly capture multi-class scenarios, which are not inclusive of the affective spectrum that memes can represent. Another observation is that memes can propagate globally through repackaging in different languages and that they can also be multilingual, blending different cultures. We conclude by highlighting several challenges related to multimodal semiotics, technological constraints, and non-trivial social engagement, and we present several open-ended aspects such as delineating online harm and empirically examining related frameworks and assistive interventions, which we believe will motivate and drive future research.

Curbing online hate speech has become the need of the hour; however, a blanket ban on such activities is infeasible for several geopolitical and cultural reasons. To reduce the severity of the problem, in this paper, we introduce a novel task, hate speech normalization, that aims to weaken the intensity of hatred exhibited by an online post. The intention of hate speech normalization is not to support hate but instead to provide the users with a stepping stone towards non-hate while giving online platforms more time to monitor any improvement in the user's behavior. To this end, we manually curated a parallel corpus - hate texts and their normalized counterparts (a normalized text is less hateful and more benign). We introduce NACL, a simple yet efficient hate speech normalization model that operates in three stages - first, it measures the hate intensity of the original sample; second, it identifies the hate span(s) within it; and finally, it reduces hate intensity by paraphrasing the hate spans. We perform extensive experiments to measure the efficacy of NACL via three-way evaluation (intrinsic, extrinsic, and human-study). We observe that NACL outperforms six baselines - NACL yields a score of 0.1365 RMSE for the intensity prediction, 0.622 F1-score in the span identification, and 82.27 BLEU and 80.05 perplexity for the normalized text generation. We further show the generalizability of NACL across other platforms (Reddit, Facebook, Gab). An interactive prototype of NACL was put together for the user study. Further, the tool is being deployed in a real-world setting at Wipro AI as a part of its mission to tackle harmful content on online platforms.

The psychotherapy intervention technique is a multifaceted conversation between a therapist and a patient. Unlike general clinical discussions, psychotherapy's core components (viz. symptoms) are hard to distinguish, thus becoming a complex problem to summarize later. A structured counseling conversation may contain discussions about symptoms, history of mental health issues, or the discovery of the patient's behavior. It may also contain discussion filler words irrelevant to a clinical summary. We refer to these elements of structured psychotherapy as counseling components. In this paper, the aim is mental health counseling summarization to build upon domain knowledge and to help clinicians quickly glean meaning. We create a new dataset after annotating 12.9K utterances of counseling components and reference summaries for each dialogue. Further, we propose ConSum, a novel counseling-component guided summarization model. ConSum undergoes three independent modules. First, to assess the presence of depressive symptoms, it filters utterances utilizing the Patient Health Questionnaire (PHQ-9), while the second and third modules aim to classify counseling components. At last, we propose a problem-specific Mental Health Information Capture (MHIC) evaluation metric for counseling summaries. Our comparative study shows that we improve on performance and generate cohesive, semantic, and coherent summaries. We comprehensively analyze the generated summaries to investigate the capturing of psychotherapy elements. Human and clinical evaluations on the summary show that ConSum generates quality summary. Further, mental health experts validate the clinical acceptability of the ConSum. Lastly, we discuss the uniqueness in mental health counseling summarization in the real world and show evidences of its deployment on an online application with the support of mpathic.ai

Social media has become the fulcrum of all forms of communication. Classifying social texts such as fake news, rumour, sarcasm, etc. has gained significant attention. The surface-level signals expressed by a social-text itself may not be adequate for such tasks; therefore, recent methods attempted to incorporate other intrinsic signals such as user behavior and the underlying graph structure. Oftentimes, the public wisdom expressed through the comments/replies to a social-text acts as a surrogate of crowd-sourced view and may provide us with complementary signals. State-of-the-art methods on social-text classification tend to ignore such a rich hierarchical signal. Here, we propose Hyphen, a discourse-aware hyperbolic spectral co-attention network. Hyphen is a fusion of hyperbolic graph representation learning with a novel Fourier co-attention mechanism in an attempt to generalise the social-text classification tasks by incorporating public discourse. We parse public discourse as an Abstract Meaning Representation (AMR) graph and use the powerful hyperbolic geometric representation to model graphs with hierarchical structure. Finally, we equip it with a novel Fourier co-attention mechanism to capture the correlation between the source post and public discourse. Extensive experiments on four different social-text classification tasks, namely detecting fake news, hate speech, rumour, and sarcasm, show that Hyphen generalises well, and achieves state-of-the-art results on ten benchmark datasets. We also employ a sentence-level fact-checked and annotated dataset to evaluate how Hyphen is capable of producing explanations as analogous evidence to the final prediction.

Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection. Today's best neural stance detectors need large volumes of training data, which is difficult to curate given the fast-changing landscape of social media text and issues on which users opine. Homophily properties over the social network provide strong signal of coarse-grained user-level stance. But semi-supervised approaches for tweet-level stance detection fail to properly leverage homophily. In light of this, We present SANDS, a new semi-supervised stance detector. SANDS starts from very few labeled tweets. It builds multiple deep feature views of tweets. It also uses a distant supervision signal from the social network to provide a surrogate loss signal to the component learners. We prepare two new tweet datasets comprising over 236,000 politically tinted tweets from two demographics (US and India) posted by over 87,000 users, their follower-followee graph, and over 8,000 tweets annotated by linguists. SANDS achieves a macro-F1 score of 0.55 (0.49) on US (India)-based datasets, outperforming 17 baselines (including variants of SANDS) substantially, particularly for minority stance labels and noisy text. Numerous ablation experiments on SANDS disentangle the dynamics of textual and network-propagated stance signals.

The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect ~ 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA.

While the rise in popularity of social media is seen as a hugely positive development, it is also accompanied by a proliferation of hate speech, which has recently become a major concern. On the one hand, hateful content creates an unsafe environment for certain members of society. On the other hand, manual moderation causes distress to content moderators, and the volume of harmful content is far beyond what human moderators can manually flag and react to. Thus, researchers in machine learning, social computing, and other areas have worked on developing tools to help automate the process. While initially studied as a text classification problem, over time, researchers realized that hate speech is multi-faceted and requires analysis of the role of linguistic expressions, context, and network structure, while using inspiration from psychology and user behavior, among others. With this in mind, we provide a holistic view of what the research community has explored so far, and what we believe are promising future research directions.

Code-mixing is the practice of alternating between two or more languages. A major part of sentiment analysis research has been monolingual and they perform poorly on the code-mixed text. We introduce methods that use multilingual and cross-lingual embeddings to transfer knowledge from monolingual text to code-mixed text for code-mixed sentiment analysis. Our methods handle code-mixed text through zero-shot learning and beat state-of-the-art English-Spanish code-mixed sentiment analysis by an absolute 3% F1-score. We are able to achieve 0.58 F1-score (without a parallel corpus) and 0.62 F1-score (with the parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available on github.com/sedflix/unsacmt.

The aim of image captioning is to generate textual description of a given image. Though seemingly an easy task for humans, it is challenging for machines as it requires the ability to comprehend the image (computer vision) and consequently generate a human-like description for the image (natural language understanding). In recent times, encoder-decoder based architectures have achieved state-of-the-art results for image captioning. Here, we present a heuristic of beam search on top of the encoder-decoder based architecture that gives better quality captions on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.

Due to the over-emphasize of the quantity of data, the data quality has often been overlooked. However, not all training data points contribute equally to learning. In particular, if mislabeled, it might actively damage the performance of the model and the ability to generalize out of distribution, as the model might end up learning spurious artifacts present in the dataset. This problem gets compounded by the prevalence of heavily parameterized and complex deep neural networks, which can, with their high capacity, end up memorizing the noise present in the dataset. This paper proposes a novel statistic - noise score, as a measure for the quality of each data point to identify such mislabeled samples based on the variations in the latent space representation. In our work, we use the representations derived by the inference network of data quality supervised variational autoencoder (AQUAVS). Our method leverages the fact that samples belonging to the same class will have similar latent representations. Therefore, by identifying the outliers in the latent space, we can find the mislabeled samples. We validate our proposed statistic through experimentation by corrupting MNIST, FashionMNIST, and CIFAR10/100 datasets in different noise settings for the task of identifying mislabelled samples. We further show significant improvements in accuracy for the classification task for each dataset.

The formulation of a claim rests at the core of argument mining. To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization. Furthermore, the increase in the usage of online social media has resulted in an explosion of unsolicited information on the web presented as informal text. To account for the aforementioned, in this paper, we propose DESYR. It is a framework that intends on annulling the said issues for informal web-based text by leveraging a combination of hierarchical representation learning (dependency-inspired Poincaré embedding), definition-based alignment, and feature projection. We do away with fine-tuning compute-heavy language models in favor of fabricating a more domain-centric but lighter approach. Experimental results indicate that DESYR builds upon the state-of-the-art system across four benchmark claim datasets, most of which were constructed with informal texts. We see an increase of 3 claim-F1 points on the LESA-Twitter dataset, an increase of 1 claim-F1 point and 9 macro-F1 points on the Online Comments (OC) dataset, an increase of 24 claim-F1 points and 17 macro-F1 points on the Web Discourse (WD) dataset, and an increase of 8 claim-F1 points and 5 macro-F1 points on the Micro Texts (MT) dataset. We also perform an extensive analysis of the results. We make a 100-D pre-trained version of our Poincaré-variant along with the source code.

Shreya Gupta, Parantak Singh, Megha Sundriyal, Md. Shad Akhtar, Tanmoy Chakraborty. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Online hate speech, particularly over microblogging platforms like Twitter, has emerged as arguably the most severe issue of the past decade. Several countries have reported a steep rise in hate crimes infuriated by malicious hate campaigns. While the detection of hate speech is one of the emerging research areas, the generation and spread of topic-dependent hate in the information network remain under-explored. In this work, we focus on exploring user behavior, which triggers the genesis of hate speech on Twitter and how it diffuses via retweets. We crawl a large-scale dataset of tweets, retweets, user activity history, and follower networks, comprising over 161 million tweets from more than 41 million unique users. We also collect over 600k contemporary news articles published online. We characterize different signals of information that govern these dynamics. Our analyses differentiate the diffusion dynamics in the presence of hate from usual information diffusion. This motivates us to formulate the modeling problem in a topic-aware setting with real-world knowledge. For predicting the initiation of hate speech for any given hashtag, we propose multiple feature-rich models, with the best performing one achieving a macro F1 score of 0.65. Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention. RETINA achieves a macro F1-score of 0.85, outperforming multiple state-of-the-art models. Our analysis reveals the superlative power of RETINA to predict the retweet dynamics of hateful content compared to the existing diffusion models.

Given a tweet, predicting the discussions that unfold around it is convoluted, to say the least. Most if not all of the discernibly benign tweets which seem innocuous may very well attract inflammatory posts (hate speech) from people who find them non-congenial. Therefore, building upon the aforementioned task and predicting if a tweet will incite hate speech is of critical importance. To stifle the dissemination of online hate speech is the need of the hour. Thus, there have been a handful of models for the detection of hate speech. Classical models work retrospectively by leveraging a reactive strategy – detection after the postage of hate speech, i.e., a backward trace after detection. Therefore, a benign post that may act as a surrogate to invoke toxicity in the near future, may not be flagged by the existing hate speech detection models. In this paper, we address this problem through a proactive strategy initiated to avert hate crime. We propose DRAGNET, a deep stratified learning framework which predicts the intensity of hatred that a root tweet can fetch through its subsequent replies. We extend the collection of social media discourse from our earlier work [1], comprising the entire reply chains up to $\sim$5k root tweets catalogued into four controversial topics Similar to [1], we notice a handful of cases where despite the root tweets being non-hateful, the succeeding replies inject an enormous amount of toxicity into the discussions. DRAGNET turns out to be highly effective, significantly outperforming six state-of-the-art baselines. It beats the best baseline with an increase of 9.4% in the Pearson correlation coefficient and a decrease of 19% in Root Mean Square Error. Further, DRAGNET’S deployment in Logically’s advanced AI platform designed to monitor real-world problematic and hateful narratives has improved the aggregated insights extracted for understanding their spread, influence and thereby offering actionable intelligence to counter them

Curbing hate speech is undoubtedly a major challenge for online microblogging platforms like Twitter. While there have been studies around hate speech detection, it is not clear how hate speech finds its way into an online discussion. It is important for a content moderator to not only identify which tweet is hateful but also to predict which tweet will be responsible for accumulating hate speech. This would help in prioritizing tweets that need constant monitoring. Our analysis reveals that for hate speech to manifest in an ongoing discussion, the source tweet may not necessarily be hateful; rather, there are plenty of such non-hateful tweets which gradually invoke hateful replies, resulting in the entire reply threads becoming provocative.

The Transformer and its variants have been proven to be efficient sequence learners in many different domains. Despite their staggering success, a critical issue has been the enormous number of parameters that must be trained (ranging from $10^7$ to $10^{11}$) along with the quadratic complexity of dot-product attention. In this work, we investigate the problem of approximating the two central components of the Transformer --- multi-head self-attention and point-wise feed-forward transformation, with reduced parameter space and computational complexity. We build upon recent developments in analyzing deep neural networks as numerical solvers of ordinary differential equations. Taking advantage of an analogy between Transformer stages and the evolution of a dynamical system of multiple interacting particles, we formulate a temporal evolution scheme, \name, to bypass costly dot-product attention over multiple stacked layers. We perform exhaustive experiments with \name\ on well-known encoder-decoder as well as encoder-only tasks. We observe that the degree of approximation (or inversely, the degree of parameter reduction) has different effects on the performance, depending on the task. While in the encoder-decoder regime, \name\ delivers performances comparable to the original Transformer, in encoder-only tasks it consistently outperforms Transformer along with several subsequent variants.

The past two decades have witnessed the rapid growth of scientific publications in all areas of research. Easier access to published literature (open access, arxiv preprints, etc.) along with the recent development in computational methods, has provided researchers with a productive platform to study vast amounts of scholarly data. Scholarly data mining has thus made it possible to do “research about research!” It plays a vital role in scientometrics, bibliometrics, webometrics, and altmetrics, which require applying sophisticated algorithms to curate and derive useful insights from scholarly data. Moreover, the knowledge extracted from the scientific data can help in several decision-making processes such as policy making for fund disbursement, identifying research gap in a department and recruiting faculties to fill up the gap, speculating upcoming research areas, etc. On the other hand, the increasing popularity and use of these metrics as a measure of the quality of research output, for determining university rankings, and in decision making (tenure and recruitment decisions), has also given rise to objectionable practices to artificially boost these measures (self-citations, citation-cliques, etc.). Given that, is it always right to consider these metrics as a reliable proxy of research quality? How should decision and policymakers use these metrics to account for such malpractices? This special section aims to bring together the latest groundbreaking research on issues related to knowledge extraction and deriving insights from scientific data. Of special interest is the role these metrics play in policy and decision making – both positives and negatives. We welcomed both theoretical and empirical research, and case studies that lead to the development of novel algorithms, tool, techniques, metrics, decisions and measurements related to scholarly data. A total of 12 papers were submitted to this special section. All the submitted papers underwent a rigorous review process. Each paper was reviewed by at least two reviewers. Every paper went through at least two rounds of revisions. The contributions of the accepted papers are briefly summarized below. Outlier detection in data mining is a major research agenda. However, when it comes to scientometric research, such outliers indicate malpractices in scientific research, resulting in issues such as citation cartels and citation stacking. Chakraborty et al. (2020) defined a diverse feature set that can identify such cases of extreme outliers and reasoned them. They also showed the effect of such outlier behaviour on the bibliographic metrics such as h-index and impact factor. Madisetty et al. (2020) proposed a tool to extract inline mathematical expressions from scientific articles. This is a major problem in scientific document processing as mathematic systems often act as a bottleneck due to their cryptic symbols that a parse is unable to extract. The authors proposed two models - the first one uses conditional random field using hand-crafted features and the second one uses bidirectional LSTM. This work contributes to building a real-world tool or can act as a plugin of a scientific document parser. Document classification is always an important problem in text processing. In scientific data mining, document classification is needed to categorize scientific papers into topics, keywords, etc. Masmoudi et al. (2020) proposed a novel hierarchical document classification approach using limited labelled data. They utilized the co-training paradigm to exploit content and bibliographic coupling information as two distinct papers' views. A massive unlabelled data was utilized during co-training. We hope that this special section will provide insights, analysis, and understanding about “scientific research” and help in building the foundation for future research and development in scientometrics. We sincerely thank the Editor-in-Chief of this journal, Dr Jon G. Hall for accepting our request to organize the special section with the Expert Systems journal. We would also like to thank the entire editorial team of the journal, the authors who submitted their valuable research to this special section and the reviews for their support for timely evaluations and comments.

Community affiliation of a node plays an important role in determining its contextual position in the network, which may raise privacy concerns when a sensitive node wants to hide its identity in a network. Oftentimes, a target community seeks to protect itself from adversaries so that its constituent members remain hidden inside the network. The current study focuses on hiding such sensitive communities so that the community affiliation of the targeted nodes can be concealed. This leads to the problem of community deception, which investigates the avenues of minimally rewiring nodes in a network so that a given target community maximally hides from a community detection algorithm (CDA). We formalize the problem of community deception and introduce Network deception using permanence loss (NEURAL), a novel method that greedily optimizes a node-centric objective function to determine the rewiring strategy. Theoretical settings pose a restriction on the number of strategies that can be employed to optimize the objective function, which in turn reduces the overhead of choosing the best strategy from multiple options. We also show that our objective function is submodular and monotone. When tested on both synthetic and seven real-world networks, NEURAL is able to deceive six widely used CDAs. We benchmark its performance with respect to four state-of-the-art methods on four evaluation metrics. In addition, our qualitative analysis on three other attributed real-world networks reveals that NEURAL, quite strikingly, captures important metainformation about edges that otherwise could not be inferred by observing only their topological structures.

In this article, we address the problem of data scarcity for the sequence classification tasks. We propose AugmentGAN, a simple-yet-effective generative adversarial network-based text augmentation model, which ensures syntactic coherency in the newly generated samples. Given an input with a label, AugmentGAN aims to generate a semantically similar sequence that follows the syntactic structure of the original sample. Exhaustive task-based evaluation is conducted to show the efficacy of AugmentGAN—we employ 12 different datasets across five classification tasks, i.e., sentiment analysis, emotion recognition, sarcasm detection, intent classification, and spam detection. We observe that, compared to the existing text augmentation techniques, AugmentGAN yields an improved performance across datasets for all the tasks. AugmentGAN also turns out to be effective for multiple languages, i.e., English, Hindi, and Bengali.

Today, major corporations and government organizations must face the reality that they will be hacked by malicious actors. In this paper, we consider the case of defending enterprises that have been successfully hacked by imposing additional a posteriori costs on the attacker. Our idea is simple: for every real document $d$ d , we develop methods to automatically generate a set $Fake(d)$ F a k e ( d ) of fake documents that are very similar to $d$ d . The attacker who steals documents must wade through a large number of documents in detail in order to separate the real one from the fakes. Our $\mathsf {FORGE}$ FORGE system focuses on technical documents (e.g., engineering/design documents) and involves three major innovations. First, we represent the semantic content of documents via multi-layer graphs (MLGs). Second, we propose a novel concept of “meta-centrality” for multi-layer graphs. A meta-centrality (MC) measure takes a classical centrality measure (for ordinary graphs, not MLGs) as input, and generalizes it to MLGs. The idea is to generate fake documents by replacing concepts on the basis of meta-centrality with related concepts according to an ontology. Our third innovation is to show that the problem of generating the set $Fake(d)$ F a k e ( d ) of fakes can be viewed as an optimization problem. We prove that this problem is NP-complete and then develop efficient heuristics to solve it in practice. We ran detailed experiments on two datasets: one a panel of 20 human subjects, another with a panel of 10. Our results show that $\mathsf {FORGE}$ FORGE generates highly believable fakes.

YouTube sells advertisements on the posted videos, which in turn enables the content creators to monetize their videos. As an unintended consequence, this has proliferated various illegal activities such as artificial boosting of views, likes, comments, and subscriptions. We refer to such videos (gaining likes and comments artificially) and channels (gaining subscriptions artificially) as “collusive entities.” Detecting such collusive entities is an important yet challenging task. Existing solutions mostly deal with the problem of spotting fake views, spam comments, fake content, and so on, and oftentimes ignore how such fake activities emerge via collusion. Here, we collect a large dataset consisting of two types of collusive entities on YouTube— videos submitted to gain collusive likes and comment requests and channels submitted to gain collusive subscriptions. We begin by providing an in-depth analysis of collusive entities on YouTube fostered by various blackmarket services . Following this, we propose models to detect three types of collusive YouTube entities: videos seeking collusive likes, channels seeking collusive subscriptions, and videos seeking collusive comments. The third type of entity is associated with temporal information. To detect videos and channels for collusive likes and subscriptions, respectively, we utilize one-class classifiers trained on our curated collusive entities and a set of novel features. The SVM-based model shows significant performance with a true positive rate of 0.911 and 0.910 for detecting collusive videos and collusive channels, respectively. To detect videos seeking collusive comments, we propose CollATe , a novel end-to-end neural architecture that leverages time-series information of posted comments along with static metadata of videos. CollATe is composed of three components: metadata feature extractor (which derives metadata-based features from videos), anomaly feature extractor (which utilizes the time-series data to detect sudden changes in the commenting activity), and comment feature extractor (which utilizes the text of the comments posted during collusion and computes a similarity score between the comments). Extensive experiments show the effectiveness of CollATe (with a true positive rate of 0.905) over the baselines.

In recent years, there has been a substantial rise in the consumption of news via online platforms. The ease of publication and lack of editorial rigour in some of these platforms have further led to the proliferation of fake news. In this paper, we study the problem of detecting fake news on the FakeNewsNet repository, a collection of full length articles along with associated images. We present SpotFake+, a multimodal approach that leverages transfer learning to capture semantic and contextual information from the news articles and its associated images and achieves the better accuracy for fake news detection. To the best of our knowledge, this is the first work that performs a multimodal approach for fake news detection on a dataset that consists of full length articles. It outperforms the performance shown by both single modality and multiple-modality models. We also release the pretrained model for the benefit of the community.

Attentional, RNN-based encoder-decoder architectures have obtained impressive performance on abstractive summarization of news articles. However, these methods fail to account for long term dependencies within the sentences of a document. This problem is exacerbated in multi-document summarization tasks such as summarizing the popular opinion in threads present in community question answering (CQA) websites such as Yahoo! Answers and Quora. These threads contain answers which often overlap or contradict each other. In this work, we present a hierarchical encoder based on structural attention to model such inter-sentence and inter-document dependencies. We set the popular pointer-generator architecture and some of the architectures derived from it as our baselines and show that they fail to generate good summaries in a multi-document setting. We further illustrate that our proposed model achieves significant improvement over the baseline in both single and multi-document summarization settings -- in the former setting, it beats the baseline by 1.31 and 7.8 ROUGE-1 points on CNN and CQA datasets, respectively; in the latter setting, the performance is further improved by 1.6 ROUGE-1 points on the CQA dataset.

Modeling user engagement dynamics on social media has compelling applications in market trend analysis, user-persona detection, and political discourse mining. Most existing approaches depend heavily on knowledge of the underlying user network. However, a large number of discussions happen on platforms that either lack any reliable social network (news portal, blogs, Buzzfeed) or reveal only partially the inter-user ties (Reddit, Stackoverflow). Many approaches require observing a discussion for some considerable period before they can make useful predictions. In real-time streaming scenarios, observations incur costs. Lastly, most models do not capture complex interactions between exogenous events (such as news articles published externally) and in-network effects (such as follow-up discussions on Reddit) to determine engagement levels. To address the three limitations noted above, we propose a novel framework, ChatterNet, which, to our knowledge, is the first that can model and predict user engagement without considering the underlying user network. Given streams of timestamped news articles and discussions, the task is to observe the streams for a short period leading up to a time horizon, then predict chatter: the volume of discussions through a specified period after the horizon. ChatterNet processes text from news and discussions using a novel time-evolving recurrent network architecture that captures both temporal properties within news and discussions, as well as influence of news on discussions. We report on extensive experiments using a two-month-long discussion corpus of Reddit, and a contemporaneous corpus of online news articles from the Common Crawl. ChatterNet shows considerable improvements beyond recent state-of-the-art models of engagement prediction. Detailed studies controlling observation and prediction windows, over 43 different subreddits, yield further useful insights.

Persuasion and argumentation are possibly among the most complex examples of the interplay between multiple human subjects. With the advent of the Internet, online forums provide wide platforms for people to share their opinions and reasonings around various diverse topics. In this work, we attempt to model persuasive interaction between users on Reddit, a popular online discussion forum. We propose a deep LSTM model to classify whether a conversation leads to a successful persuasion or not, and use this model to predict whether a certain chain of arguments can lead to persuasion. While learning persuasion dynamics, our model tends to identify argument facets implicitly, using an attention mechanism. We also propose a semi-supervised approach to extract argumentative components from discussion threads. Both these models provide useful insight into how people engage in argumentation on online discussion forums.

Abstract Most of the community detection algorithms assume that thecompletenetwork structure $$\mathcal {G}=(\mathcal {V},\mathcal {E})$$ G=(V,E) is available in advance for analysis. However, in reality this may not be true due to several reasons, such as privacy constraints and restricted access, which result in a partial snapshot of the entire network. In addition, we may be interested in identifying the community information of only a selected subset of nodes (denoted by $$\mathcal {V}_{{\mathrm{T}}} \subseteq \mathcal {V}$$ VT⊆V ), rather than obtaining the community structure of all the nodes in $$\mathcal {G}$$ G . To this end, we propose an incremental community detection method that repeats two stages—(i) network scan and (ii) community update. In the first stage, our method selects an appropriate node in such a way that the discovery of its local neighborhood structure leads to an accurate community detection in the second stage. We propose a novel criterion, calledInformation Gain, based on existing network embedding algorithms (Deepwalk and node2vec) to scan a node. The proposed community update stage consists of expectation–maximization and Markov Random Field-based denoising strategy. Experiments with 5 diverse networks with known ground-truth community structure show that our algorithm achieves 10.2% higher accuracy on average over state-of-the-art algorithms for both network scan and community update steps.

Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology that not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric, Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution).

Online marketplaces often witness opinion spam in the form of reviews. People are often hired to target specific brands for promoting or impeding them by writing highly positive or negative reviews. This often is done collectively in groups. Although some previous studies attempted to identify and analyze such opinion spam groups, little has been explored to spot those groups who target a brand as a whole, instead of just products. In this article, we collected the reviews from the Amazon product review site and manually labeled a set of 923 candidate reviewer groups. The groups are extracted using frequent itemset mining over brand similarities such that users are clustered together if they have mutually reviewed (products of) a lot of brands. We hypothesize that the nature of the reviewer groups is dependent on eight features specific to a (group, brand) pair. We develop a feature-based supervised model to classify candidate groups as extremist entities. We run multiple classifiers for the task of classifying a group based on the reviews written by the users of that group to determine whether the group shows signs of extremity. A three-layer perceptron-based classifier turns out to be the best classifier. We further study behaviors of such groups in detail to understand the dynamics of brand-level opinion fraud better. These behaviors include consistency in ratings, review sentiment, verified purchase, review dates, and helpful votes received on reviews. Surprisingly, we observe that there are a lot of verified reviewers showing extreme sentiment, which, on further investigation, leads to ways to circumvent the existing mechanisms in place to prevent unofficial incentives on Amazon.

As the most widely used mobile platform, Android is also the biggest target for mobile malware. Given the increasing number of Android malware variants, detecting malware families is crucial so that security analysts can identify situations where signatures of a known malware family can be adapted as opposed to manually inspecting behavior of all samples. We present EC2 (Ensemble Clustering and Classification), a novel algorithm for discovering Android malware families of varying sizes-ranging from very large to very small families (even if previously unseen). We present a performance comparison of several traditional classification and clustering algorithms for Android malware family identification on DREBIN, the largest public Android malware dataset with labeled families. We use the output of both supervised classifiers and unsupervised clustering to design EC2. Experimental results on both the DREBIN and the more recent Koodous malware datasets show that EC2 accurately detects both small and large families, outperforming several comparative baselines. Furthermore, we show how to automatically characterize and explain unique behaviors of specific malware families, such as FakeInstaller, MobileTx, Geinimi. In short, EC2 presents an early warning system for emerging new malware families, as well as a robust predictor of the family (when it is not new) to which a new malware sample belongs, and the design of novel strategies for data-driven understanding of malware behaviors.

The growth of online social media has led to a huge increase in the number of users who want to share and publicize various kinds of information. Twitter, the most popular micro-blogging platform, has become a hotbed for users who are involved in different activities such as news publishing, job hunting, recruiting, advertising and publicity. Retweeting a tweet is a major action to broadcast a user's message out to millions of users. Retweet action has two major advantages: (i) gaining quick exposure to the content, and (ii) increasing likelihood of gaining new Twitter followers in return. The organic way of gaining a larger number of retweets is a time consuming process, which leads to the creation of unfair methods to gain retweets. Thus, Twitter users often approach various blackmarket services to gain retweets inorganically in a short duration. Blackmarkets spread their collusive ecosystem in such a way that Twitter is unable to detect them even after devoting significant effort to purge the platform off bots, trolls, and fake accounts. One major reason behind the evasion is that the collusive users involved in blackmarket services exhibit a mix of organic and inorganic behavior - they organically reweet some genuine tweets; at the same time, they inorganically retweet tweets submitted to blackmarket services. This paper is the first attempt to provide a thorough study of the collusive users involved in two types of blackmarket services - Premium and Freemium. We collect a novel dataset of collusive users comprising of users from both types of blackmarket services. We provide network-centric, profile-centric, timeline-centric and retweet-centric characteristics of these users and show how users involved in premium blackmarket services exhibit diverse behavior as compared to those involved in freemium services. We further employ human annotators to label collusive users into three types: bots, promotional customers, and normal customers. We then curate 63 novel features to run state-of-the-art classifiers in two settings - binary classification (collusive vs. genuine) and multi-class classification (bot, promotional customers, normal customers, and genuine users). Bagging achieves the best accuracy (macro F1-score of 0.892) in the former setting, whereas Random Forest outperforms others (macro F1-score of 0.791) in the latter setting. We also develop a chrome extension, SCoRe++ which can detect collusive retweeters in real time.

Retweets are essential to boost the popularity of a tweet, and a large number of fake retweeters can contribute heavily to this aspect. We define a fake retweeter as a Twitter account that retweets spammy tweets, retweets an abnormally large amount of tweets in a short period, or misuses a trending hashtag to promote events irrelevant to the topic of discussion. We introduce an up-to-date, temporally diverse, trend-oriented labeled dataset to address the problem of fake retweeter detection. We develop a novel classifier, called HawkesEye which makes predictions based on a temporal window, in contrast to existing approaches which require agraph-likerelationship between tweet entities, or the presence of theentire retweeting timelineof a retweeter. HawkesEye utilizes both temporal and textual information using a class-specific topic model and Hawkes processes. Experiments on our curated dataset show significant improvement over four state-of-the-art methods, with precision and recall scores of 0.964 and 0.960 on a balanced dataset, respectively – HawkesEye beats the best baseline by 6.16% and 25.98% relative improvement in terms of precision and recall, respectively. We also diagnose our model to understand the advantages and pitfalls of the underlying mechanism. We believe that the extent of this study is not restricted to Twitter, but generalizable to other social media systems such as Facebook and Instagram with similar reposting capabilities.

With the rise in popularity of social media platforms like Twitter, having higher influence on these platforms has a greater value attached to it, since it has the power to influence many decisions in the form of brand promotions and shaping opinions. However, blackmarket services that allow users to inorganically gain influence are a threat to the credibility of these social networking platforms. Twitter users can gain inorganic appraisals in the form of likes, retweets, and follows through these blackmarket services either by paying for them or by joining syndicates wherein they gain such appraisals by providing similar appraisals to other users. These customers tend to exhibit a mix of organic and inorganic retweeting behavior, making it tougher to detect them. In this article, we investigate these blackmarket customers engaged in collusive retweeting activities. We collect and annotate a novel dataset containing various types of information about blackmarket customers and use these sources of information to construct multiple user representations. We adopt Weighted Generalized Canonical Correlation Analysis (WGCCA) to combine these individual representations to derive user embeddings that allow us to effectively classify users as: genuine users, bots, promotional customers, and normal customers. Our method significantly outperforms state-of-the-art approaches (32.95% better macro F1-score than the best baseline).

In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole passages (say, with sentiments), which we would like to exploit toward better position-specific label inference (say, target-dependent sentiment annotation). A key characteristic shared across such tasks is that different positions in a primary instance can benefit from different ‘experts’ trained from auxiliary data, but labeled primary instances are scarce, and labeling the best expert for each position entails unacceptable cognitive burden. We propose GIRNet, a unified position-sensitive multi-task recurrent neural network (RNN) architecture for such applications. Auxiliary and primary tasks need not share training instances. Auxiliary RNNs are trained over auxiliary instances. A primary instance is also submitted to each auxiliary RNN, but their state sequences are gated and merged into a novel composite state sequence tailored to the primary inference task. Our approach is in sharp contrast to recent multi-task networks like the crossstitch and sluice networks, which do not control state transfer at such fine granularity. We demonstrate the superiority of GIRNet using three applications: sentiment classification of code-switched passages, part-of-speech tagging of codeswitched text, and target position-sensitive annotation of sentiment in monolingual passages. In all cases, we establish new state-of-the-art performance beyond recent competitive baselines.

Recent research on community detection focuses on learning representations of nodes using different network embedding methods, and then feeding them as normal features to clustering algorithms. However, we find that though one may have good results by direct clustering based on such network embedding features, there is ample room for improvement. More seriously, in many real networks, some statisticallysignificant nodes which play pivotal roles are often divided into incorrect communities using network embedding methods. This is because while some distance measures are used to capture the spatial relationship between nodes by embedding, the nodes after mapping to feature vectors are essentially not coupled any more, losing important structural information. To address this problem, we propose a general Markov Random Field (MRF) framework to incorporate coupling in network embedding which allows better detecting network communities. By smartly utilizing properties of MRF, the new framework not only preserves the advantages of network embedding (e.g. low complexity, high parallelizability and applicability for traditional machine learning), but also alleviates its core drawback of inadequate representations of dependencies via making up the missing coupling relationships. Experiments on real networks show that our new approach improves the accuracy of existing embedding methods (e.g. Node2Vec, DeepWalk and MNMF), and corrects most wrongly-divided statistically-significant nodes, which makes network embedding essentially suitable for real community detection applications. The new approach also outperforms other state-of-the-art conventional community detection methods.

Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences. Twitter is one such popular platform where people publish tweets to spread their messages to everyone. Twitter allows users to Retweet other users' tweets in order to broadcast it to their network. The more retweets a particular tweet gets, the faster it spreads. This creates incentives for people to obtain artificial growth in the reach of their tweets by using certain blackmarket services to gain inorganic appraisals for their content.

Over the last decade, online forums have become primary news sources for readers around the globe, and social media platforms are the space where these news forums find most of their audience and engagement. Our particular focus in this paper is to study conflict dynamics over online news articles in Reddit, one of the most popular online discussion platforms. We choose to study how conflicts develop around news inside a discussion community, the \em r/news subreddit. Mining the characteristics of these engagements often provide useful insights into the behavioral dynamics of large-scale human interactions. Such insights are useful for many reasons -- for news houses to improvise their publishing strategies and potential audience, for data analytics to get a better introspection over media engagement as well as for social media platforms to avoid unnecessary and perilous conflicts. In this work, we present a novel quantification of conflict in online discussion. Unlike previous studies on conflict dynamics, which model conflict as a binary phenomenon, our measure is continuous-valued, which we validate with manually annotated ratings. We address a two-way prediction task. Firstly, we predict the probable degree of conflict a news article will face from its audience. We employ multiple machine learning frameworks for this task using various features extracted from news articles.Secondly, given a pair of users and their interaction history, we predict if their future engagement will result in a conflict. We fuse textual and network-based features together using a support vector machine which achieves an AUC of 0.89. Moreover, we implement a graph convolutional model which exploits engagement histories of users to predict whether a pair of users who never met each other before will have a conflicting interaction, with an AUC of 0.69. We perform our studies on a massive discussion dataset crawled from the Reddit news community, containing over $41k$ news articles and $5.5$ million comments. Apart from the prediction tasks, our studies offer interesting insights on the conflict dynamics -- how users form clusters based on conflicting engagements, how different is the temporal nature of conflict over different online news forums, how is contribution of different language based features to induce conflict, etc. In short, our study paves the way towards new methods of exploration and modeling of conflict dynamics inside online discussion communities.

Online discussions are valuable resources to study user behaviour on a diverse set of topics. Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two inter-related problems - predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments. We propose RGNet (Relativistic Gravitational Nerwork), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as 'cloud of dust' hovering over a user spacetime manifold, attracting users of different groups at different rates over time. We also propose GUVec, a global user embedding method for an online discussion, which is used by RGNet to predict temporal user engagement. RGNet leverages different textual and network-based features to learn the dust distribution for discussions. We employ four baselines - first two using LSTM architecture, third one using Newtonian model of gravity, and fourth one using a logistic regression adopted from a previous work on engagement prediction. Experiments on Reddit dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error for temporal engagement prediction of user groups and growth rate forecasting, respectively, outperforming all the baselines significantly. We further employ RGNet to predict non-temporal engagement - whether users will comment to a given post or not. RGNet achieves 0.62 AUC for this task, outperforming existing baseline by 8.77% AUC.

Online reviews play a crucial role in deciding the quality before purchasing any product. Unfortunately, spammers often take advantage of online review forums by writing fraud reviews to promote/demote certain products. It may turn out to be more detrimental when such spammers collude and collectively inject spam reviews as they can take complete control of users' sentiment due to the volume of fraud reviews they inject. Group spam detection is thus more challenging than individual-level fraud detection due to unclear definition of a group, variation of inter-group dynamics, scarcity of labeled group-level spam data, etc. Here, we propose DeFrauder, an unsupervised method to detect online fraud reviewer groups. It first detects candidate fraud groups by leveraging the underlying product review graph and incorporating several behavioral signals which model multi-faceted collaboration among reviewers. It then maps reviewers into an embedding space and assigns a spam score to each group such that groups comprising spammers with highly similar behavioral traits achieve high spam score. While comparing with five baselines on four real-world datasets (two of them were curated by us), DeFrauder shows superior performance by outperforming the best baseline with 17.11% higher NDCG@50 (on average) across datasets.

Traditional forms of education are increasingly being replaced by online forms of learning. With many degrees being awarded without the requirement of co-location, it becomes necessary to build tools to enhance online learning interfaces. Online educational videos are often long and do not have enough metadata. Viewers trying to learn about a particular topic have to go through the entire video to find suitable content. We present a novel architecture to curate content tables for educational videos. We harvest text and acoustic properties of the videos to form a hierarchical content table (similar to a table of contents available in a textbook). We allow users to browse the video smartly by skipping to a particular portion rather than going through the entire video. We consider other text-based approaches as our baselines. We find that our approach beats the macro F1-score and micro F1-score of baseline by 39.45% and 35.76% respectively. We present our demo as an independent web page where the user can paste the URL of the video to obtain a generated hierarchical table of contents and navigate to the required content. In the spirit of reproducibility, we make our code public at https://goo.gl/Qzku9d and provide a screen cast to be viewed at https://goo.gl/4HSV1v.

We address the problem of automating the process of network troubleshooting for a large-scale WiFi network. Specifically, we target identifying the causes of unnecessary active scans in WiFi networks, that are known to degrade the WiFi performance. We collect 340 hours worth of data with several thousands of episodes of active scans to train various machine learning models. Data is collected with 27 devices across vendors in varied network setups under a controlled setting. We study unsupervised and supervised machine learning techniques to conclude that a multilayer perceptron is the best model to detect the causes of active scanning. Further, we perform an in-vivo model validation in an uncontrolled real-world WiFi network. We also compare our models with a static rule-based approach. Our model improves the mean F1-score accuracy of cause detection from 0.78 to 0.99. Our proposed mechanism has the potential of being incorporated in the existing WiFi controllers, such as that of Cisco and Aruba.

Twitter's popularity has fostered the emergence of various illegal user activities - one such activity is to artificially bolster visibility of tweets by gaining large number of retweets within a short time span. The natural way to gain visibility is time-consuming. Therefore, users who want their tweets to get quick visibility try to explore shortcuts - one such shortcut is to approach the blackmarket services, and gain retweets for their own tweets by retweeting other customers' tweets. Thus the users intrinsically become a part of a collusive ecosystem controlled by these services. In this paper, we propose CoReRank, an unsupervised framework to detect collusive users (who are involved in producing artificial retweets), and suspicious tweets (which are submitted to the blackmarket services) simultaneously. CoReRank leverages the retweeting (or quoting) patterns of users, and measures two scores - the 'credibility' of a user and the 'merit' of a tweet. We propose a set of axioms to derive the interdependency between these two scores, and update them in a recursive manner. The formulation is further extended to handle the cold start problem. CoReRank is guaranteed to converge in a finite number of iterations and has linear time complexity. We also propose a semi-supervised version of CoReRank (called CoReRank+) which leverages a partial ground-truth labeling of users and tweets. Extensive experiments are conducted to show the superiority of CoReRank compared to six baselines on a novel dataset we collected and annotated. CoReRank beats the best unsupervised baseline method by 269% (20%) (relative) average precision and 300% (22.22%) (relative) average recall in detecting collusive (genuine) users. CoReRank+ beats the best supervised baseline method by 33.18% AUC. CoReRank also detects suspicious tweets with 0.85 (0.60) average precision (recall). To our knowledge, CoReRank is the first unsupervised method to detect collusive users and suspicious tweets simultaneously with theoretical guarantees.

Automatic estimation of relative difficulty of a pair of questions is an important and challenging problem in community question answering (CQA) services. There are limited studies that addressed this problem. Past studies mostly leveraged expertise of users answering the questions and barely considered other properties of CQA services such as metadata of users and posts, temporal information, and textual content. In this article, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem. DiffQue starts by constructing a novel network structure that captures different notions of difficulties among a pair of questions. It then measures the relative difficulty of two questions by predicting the direction of a (virtual) edge connecting these two questions in the network. It leverages features extracted from the network structure, metadata of users/posts, and textual description of questions and answers. Experiments on datasets obtained from two CQA sites (further divided into four datasets) with human annotated ground-truth show that DiffQue outperforms four state-of-the-art methods by a significant margin (28.77% higher F 1 score and 28.72% higher AUC than the best baseline). As opposed to the other baselines, (i) DiffQue appropriately responds to the training noise, (ii) DiffQue is capable of adapting multiple domains (CQA datasets), and (iii) DiffQue can efficiently handle the “cold start” problem that may arise due to the lack of information for newly posted questions or newly arrived users.

Twitter has increasingly become a popular platform to share news and user opinion. A tweet is considered to be important if it receives high number of affirmative reactions from other Twitter users via Retweets. Retweet count is thus considered as a surrogate measure for positive crowd-sourced reactions - high number of retweets of a tweet not only help the tweet being broadcasted, but also aid in making its topic trending. This in turn bolsters the social reputation of the author of the tweet. Since social reputation/impact of users/t weets influences many decisions (such as promoting brands, advertisement etc.), several blackmarket syndicates have actively been engaged in producing fake retweets in a collusive manner. Users who want to boost the impact of their tweets approach the blackmarket services, and gain retweets for their own tweets by retweeting other customers' tweets. Thus they become customers of blackmarket syndicates and engage in fake activities. Interestingly, these customers are neither bots, nor even fake users - they are usually normal human beings; they express a mix of organic and inorganic retweeting activities, and there is no synchronicity across their behaviors. In this paper, we make a first attempt to investigate such blackmarket customers engaged in producing fake retweets. We collected and annotated a novel dataset comprising of customers of many blackmarket services and characterize them using a set of 64 novel features. We show how their social behavior differs from genuine users. We then use state-of-the-art supervised models to detect three types of customers (bots, promotional, normal) and genuine users. We achieve a Macro Fl-score of 0.87 with SVM, outperforming four other baselines significantly. We further design a browser extension, SCoRe which, given the link of a tweet, spots its fake retweeters in real-time. We also collected users' feedback on the performance of SCoRe and obtained 85% accuracy.

In this paper we introduce a methodology to identify communities in dark multilayered networks, taking into account that the main challenges of these networks are incompleteness, fuzzy boundaries, and dynamic behavior. To account for these characteristics, we create knowledge sharing communities (KSC) that determine the community detection. KSC is driven by weighing the edge attributes as desired for the application that the communities are used. We provide an interactive algorithm that allows the operator to decide on the weights and the thresholds applied to create the communities. By choosing these variables, our results quantitatively outperform community detection on the collapsed monoplex network.

The problem of imbalanced classes arises frequently in binary classification tasks. If one class outnumbers another, trained classifiers become heavily biased towards the majority class. For phishing URL detection, it is very natural that the number of collected benign URLs (i.e., the majority class) is much larger than the number of collected phishy URLs (i.e., the minority class). Oversampling the minority class can be a powerful tool to overcome this situation. However, existing methods perform the oversampling task in the feature space where the original data format is removed and URLs are succinctly represented by vectors. These methods are successful only if feature definitions are correct and the dataset is diverse and not too sparse. In this paper, we propose an oversampling technique in the data space. We train text generative adversarial networks (text-GANs) with URLs in the minority class and generate synthetic URLs that can be made part of the training set. We crawl a crowd-sourced URL repository to collect recently discovered phishy and benign URLs. Our experiments demonstrate significant performance improvements after using the proposed oversampling technique. Interestingly, some of the original test URLs are exactly regenerated by the proposed text generative model.

It is well-known that GANs are difficult to train, and several different techniques have been proposed in order to stabilize their training. In this paper, we propose a novel training method called manifold-matching, and a new GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds representing the vector representations of real and fake images. If these two manifolds match, it means that real and fake images are statistically identical. To assist the manifold-matching task, we also use i) kernel tricks to find better manifold structures, ii) moving-averaged manifolds across mini-batches, and iii) a regularizer based on correlation matrix to suppress mode collapse. We conduct in-depth experiments with three image datasets and compare with several state-of-the-art GAN models. 32.4% of images generated by the proposed MMGAN are recognized as fake images during our user study (16% enhancement compared to other state-of-the-art model). MMGAN achieved an unsupervised inception score of 7.8 for CIFAR-10.

Cybercriminals have leveraged the popularity of a large user base available on Online Social Networks~(OSNs) to spread spam campaigns by propagating phishing URLs, attaching malicious contents, etc. However, another kind of spam attacks using phone numbers has recently become prevalent on OSNs, where spammers advertise phone numbers to attract users» attention and convince them to make a call to these phone numbers. The dynamics of phone number based spam is different from URL-based spam due to an inherent trust associated with a phone number. While previous work has proposed strategies to mitigate URL-based spam attacks, phone number based spam attacks have received less attention.

Most past work on honeypots has made two assumptions: (i) they assume that the only defensive measure used is a honeypot mechanism, and (ii) they do not consider both rational and subrational adversaries and do not reason with an adversary model when placing honeypots. However, real-world system security officers use a mix of instruments such as traditional defenses (e.g. firewalls, intrusion detection systems), and honeypots form only one portion of the strategy. Moreover, the placement of traditional defenses and honeypots cannot be done independently. In this paper, we consider a Stackelberg-style game situation where the defender models the attacker and uses that model to identify the best placement of traditional defenses and honeypots. We provide a formal definition of undamaged asset value (i.e. the value that is not compromised by the attacker) under a given defensive strategy and show that the problem of finding the best placement so as to maximize undamaged asset value is NP-hard. We propose a greedy algorithm and show via experiments, both on real enterprise networks and on ones generated by the well-known network simulation tool NS-2, that our algorithm quickly computes near optimal placements. As such, our method is both practical and effective.

It has been reported that embedded URLs and multimodal content (images, video, and sound recordings) in tweets are increasingly used to seduce users into a “wrong click,” leading to malware infection. In this paper, we predict whether a tweet is malicious or not by examining five classes of features: textual content including sentiment, paths emanating from a URL mentioned in the tweet, attributes associated with URLs, and multimodal content in the tweet. A fifth class of features first constructs a novel “tweet graph” and then defines features by analyzing “metapaths” contained in the tweet graph. Next, we propose a MALicious Tweets in Parallel (${\mathit {MALT^{P}}}$) collective classification algorithm that merges together tweet graphs, metapaths, and collective classification proposed previously in the literature. We conduct detailed experiments using two data sets—Warningbird (WB) and KBA. We show that our metapath-based approach outperforms past efforts at identifying malicious tweets and further show that metapath-based features in conjunction with Alexa ranks and features from KBA yield very high predictive accuracy—over 0.98 on KBA and over 0.94 on KBA, outperforming past work. More significantly, metapath features alone generate a predictive accuracy of 0.977 and 0.923, respectively, on the KBA and WB data sets, significantly outperforming the other methods in isolation. We conduct a further analysis to identify the most important features; surprisingly, our results show that the presence of multimodal content is not a major factor and that metapath-based features dominate in separating malicious from benign tweets.

A community in a social network is considered to be a group of nodes densely connected internally and sparsely connected externally.Although previous work intensely studied network topology within a community, its semantic interpretation is hardly understood. In this paper, we attempt to understand whether individuals in a community possess similar Personalities, Values and Ethical background. Finally, we show that Personality and Values models could be used as features to discover more accurate community structure compared to the one obtained from only network information.

The Islamic State of Iraq and Syria (ISIS) is a Salafi jihadist militant group that has made extensive use of online social media platforms to promulgate its ideologies and evoke many individuals to support the organization. The psycho-sociological background of an individual plays a crucial role in determining his/her vulnerability of being lured into joining the organisation and indulge in terrorist activities, since his/her behavior largely depends on the society s/he was brought up in. Here, we analyse five sociological aspects -- personality, values & ethics, optimism/pessimism, age and gender to understand the psycho-sociological vulnerability of individuals over Twitter. Experimental results suggest that psycho-sociological aspects indeed act as foundation to discover and differentiate between prominent and unobtrusive users in Twitter.

We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental analysis comparing EC3 and iEC3 with 12 baseline methods on 13 standard benchmark datasets. We show that our methods outperform other baselines for every single dataset, achieving at most 10% higher AUC. Moreover our methods are faster, more resilient to noise and class imbalance than the best baseline method.

Individuals have distinctive ways of speaking and writing, and there exists a long history of linguistic and stylistic investigation into authorship attribution. Most authorship identification approaches are exclusively based on lexical measures such as vocabulary richness and lexico-syntactic features, or substantially generate relevant features for different machine learning approaches. These techniques are inefficient without suitable feature selection or large corpus. In this paper, we introduce three graph based models for the task of authorship identification, that consider the interaction of character sequences, phraseological patterns and structure of the sentences in the document to construct graphs separately for an author. For each model, all such graphs for different authors are aggregated to generate a combined weighted training graph. Then a simple graph traversal algorithm is used to compare testing graph with the training graph. The experiment is conducted on the documents of six authors collected from Bengali literature. Experimental results show that our models significantly outperform four state-of-the-art models (9.89% higher than the best baseline model) even for short training dataset.

Given a set AL of community detection algorithms and a graph G as inputs, we propose two ensemble methods EnDisCo and MeDOC that (respectively) identify disjoint and overlapping communities in G. EnDisCo transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. MeDOC groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare EnDisCo and MeDOC with a recent ensemble method for disjoint community detection and show that our approaches achieve superior performance. To the best of our knowledge, MeDOC is the first ensemble approach for overlapping community detection.

Research accomplishment is usually measured by considering all citations with equal importance, thus ignoring the wide variety of purposes an article is being cited for. Here, we posit that measuring the intensity of a reference is crucial not only to perceive better understanding of research endeavor, but also to improve the quality of citation-based applications. To this end, we collect a rich annotated dataset with references labeled by the intensity, and propose a novel graph-based semi-supervised model, GraLap to label the intensity of references. Experiments with AAN datasets show a significant improvement compared to the baselines to achieve the true labels of the references (46% better correlation). Finally, we provide four applications to demonstrate how the knowledge of reference intensity leads to design better real-world applications.

Several real-life social systems witness the presence of multiple interaction types (or layers) among the entities, thus establishing a collection of co-evolving networks, known as multiplex networks. More recently, there has been a significant interest in developing certain centrality measures in multiplex networks to understand the influential power of the entities (to be referred as vertices or nodes hereafter). In this paper, we consider the problem of studying how frequently the nodes occur on the shortest paths between other nodes in the multiplex networks. As opposed to simplex networks, the shortest paths between nodes can possibly traverse through multiple layers in multiplex networks. Motivated by this phenomenon, we propose a new metric to address the above problem and we call this new metric cross-layer betweenness centrality (CBC). Our definition of CBC measure takes into account the interplay among multiple layers in determining the shortest paths in multiplex networks. We propose an efficient algorithm to compute CBC and show that it runs much faster than the naïve computation of this measure. We show the efficacy of the proposed algorithm using thorough experimentation on two real-world multiplex networks. We further demonstrate the practical utility of CBC by applying it in the following three application contexts: discovering non-overlapping community structure in multiplex networks, identifying interdisciplinary researchers from a multiplex co-authorship network, and the initiator selection for message spreading. In all these application scenarios, the respective solution methods based on the proposed CBC are found to be significantly better performing than that of the corresponding benchmark approaches.

The goal of community detection algorithms is to identify densely connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily made to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities, for example modularity, are global. These metrics produce a score for each community, not for each individual node. In this article, we argue that the belongingness of nodes in a community is not uniform. We quantify the degree of belongingness of a vertex within a community by a new vertex-based metric called permanence . The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors (i) the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. We present the formulation of permanence based on these two quantities. We demonstrate that compared to other existing metrics (such as modularity, conductance, and cut-ratio), the change in permanence is more commensurate to the level of perturbation in ground-truth communities. We discuss how permanence can help us understand and utilize the structure and evolution of communities by demonstrating that it can be used to -- (i) measure the persistence of a vertex in a community, (ii) design strategies to strengthen the community structure, (iii) explore the core-periphery structure within a community, and (iv) select suitable initiators for message spreading. We further show that permanence is an excellent metric for identifying communities. We demonstrate that the process of maximizing permanence (abbreviated as MaxPerm ) produces meaningful communities that concur with the ground-truth community structure of the networks more accurately than eight other popular community detection algorithms. Finally, we provide mathematical proofs to demonstrate the correctness of finding communities by maximizing permanence. In particular, we show that the communities obtained by this method are (i) less affected by the changes in vertex ordering, and (ii) more resilient to resolution limit, degeneracy of solutions, and asymptotic growth of values.

Detection of non-overlapping and overlapping communities are essentially the same problem. However, current algorithms focus either on finding overlapping or non-overlapping communities. We present a generalized framework that can identify both non-overlapping and overlapping communities, without any prior input about the network or its community distribution. To do so, we introduce a vertex-based metric,GenPerm, that quantifies by how much a vertex belongs to each of its constituent communities. Our community detection algorithm is based on maximizing the GenPerm over all the vertices in the network. We demonstrate, through experiments over synthetic and real-world networks, that GenPerm is more effective than other metrics in evaluating community structure. Further, we show that due to its vertex-centric property, GenPerm can be used to unfold several inferences beyond community detection, such as core-periphery analysis and message spreading. Our algorithm for maximizing GenPerm outperforms six state-of-the-art algorithms in accurately predicting the ground-truth labels. Finally, we discuss the problem of resolution limit in overlapping communities and demonstrate that maximizing GenPerm can mitigate this problem.

The impact and significance of a scientific publication is measured mostly by the number of citations it accumulates over the years. Early prediction of the citation profile of research articles is a significant as well as challenging problem. In this paper, we argue that features gathered from the citation contexts of the research papers can be very relevant for citation prediction. Analyzing a massive dataset of nearly 1.5 million computer science articles and more than 26 million citation contexts, we show that average countX (number of times a paper is cited within the same article) and average citeWords (number of words within the citation context) discriminate between various citation ranges as well as citation categories. We use these features in a stratified learning framework for future citation prediction. Experimental results show that the proposed model significantly outperforms the existing citation prediction models by a margin of 8-10% on an average under various experimental settings. Specifically, the features derived from the citation context help in predicting long-term citation behavior.

Performing literature survey for scholarly activities has become a challenging and time consuming task due to the rapid growth in the number of scientific articles. Thus, automatic recommendation of high quality citations for a given scientific query topic is immensely valuable. The state-of-the-art on the problem of citation recommendation suffers with the following three limitations. First, most of the existing approaches for citation recommendation require input in the form of either the full article or a seed set of citations, or both. Nevertheless, obtaining the recommendation for citations given a set of keywords is extremely useful for many scientific purposes. Second, the existing techniques for citation recommendation aim at suggesting prestigious and well-cited articles. However, we often need recommendation of diversified citations of the given query topic for many scientific purposes; for instance, it helps authors to write survey papers on a topic and it helps scholars to get a broad view of key problems on a topic. Third, one of the problems in the keyword based citation recommendation is that the search results typically would not include the semantically correlated articles if these articles do not use exactly the same keywords. To the best of our knowledge, there is no known citation recommendation system in the literature that addresses the above three limitations simultaneously. In this paper, we propose a novel citation recommendation system called DiSCern to precisely address the above research gap. DiSCern finds relevant and diversified citations in response to a search query, in terms of keyword(s) to describe the query topic, while using only the citation graph and the keywords associated with the articles, and no latent information. We use a novel keyword expansion step, inspired by community finding in social network analysis, in DiSCern to ensure that the semantically correlated articles are also included in the results. Our proposed approach primarily builds on the Vertex Reinforced Random Walk (VRRW) to balance prestige and diversity in the recommended citations. We demonstrate the efficacy of DiSCern empirically on two datasets: a large publication dataset of more than 1.7 million articles in computer science domain and a dataset of more than 29,000 articles in theoretical high-energy physics domain. The experimental results show that our proposed approach is quite efficient and it outperforms the state-of-the-art algorithms in terms of both relevance and diversity.

The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one's co-authorship network arranged into a set of ``circles'', which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks. In this paper, we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. Our model is an unsupervised method which combines a variety of node features and node similarity measures. The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. In the first level of evaluation, our model achieves 13.33% improvement in terms of overlapping modularity compared to the best among four state-of-the-art community detection methods. Further, we conduct a task-based evaluation -- two basic frameworks for collaboration prediction are considered with the circle information (obtained from our model) included in the feature set. Experimental results show that including the circle information detected by our model improves the prediction performance by 9.87% and 15.25% on average in terms of AUC (Area under the ROC) and [email protected] (Precision at Top 20) respectively compared to the case, where the circle information is not present.

One of the key aspects instrumental in the advancement of science relates to “team science,” or in other words “group” collaborations. There have been extensive studies analyzing various statistical properties of collaborations of individual or pairs of authors. However, the number of studies pertaining to groups/teams of scientists working together is limited in number. In this paper, we set an objective to study the productivity of group collaborations where groups are represented as small substructures usually termed as network motifs in the literature. A preliminary observation is that star-like motifs have the largest productivity (defined as a function of citation count) followed by 4-cliques. We then introduce a bunch of features and study their individual relations with the productivity of a team. Building on these observations, we develop a supervised classification model that can automatically distinguish the highly productive teams from the low productive ones based on the set of identified features. The accuracy of the classification is 82% on an average for all the motifs with the accuracy reaching as high as 95% for 4-cliques. Finally, we present a detailed analysis of the time-transition behavior of different motifs along with some of the real world highly productive motifs found in our dataset. This empirical study is a first step toward the development of a full-fledged recommendation system that can predict how productive a team would be in the future.

Despite the prevalence of community detection algorithms, relatively less work has been done on understanding whether a network is indeed modular and how resilient the community structure is under perturbations. To address this issue, we propose a new vertex-based metric called "permanence", that can quantitatively give an estimate of the community- like structure of the network.

Citation analysis is a popular area of research, which has been usually used to rank the authors and the publication venues of research papers. With huge number of publications every year, it has become difficult for the users to find relevant publication materials. One simple solution to this problem is to detect communities from the citation network and recommend papers based on the common membership in communities. But, in today's research scenario, many researchers' fields of interest spread into multiple research directions resulting in an increasing number of interdisciplinary publications. Therefore, it is necessary to detect overlapping communities for relevant recommendation.

Study of community in time-varying graphs has been limited to its detection and identification across time. However, presence of time provides us with the opportunity to analyze the interaction patterns of the communities, understand how each individual community grows/shrinks, becomes important over time. This paper, for the first time, systematically studies the temporal interaction patterns of communities using a large scale citation network (directed and unweighted) of computer science. Each individual community in a citation network is naturally defined by a research field -- i.e., acting as ground-truth -- and their interactions through citations in real time can unfold the landscape of dynamic research trends in the computer science domain over the last fifty years. These interactions are quantified in terms of a metric called inwardness that captures the effect of local citations to express the degree of authoritativeness of a community (research field) at a particular time instance. Several arguments to unveil the reasons behind the temporal changes of inwardness of different communities are put forward using exhaustive statistical analysis. The measurements (importance of field) are compared with the project funding statistics of NSF and it is found that the two are in sync. We believe that this measurement study with a large real-world data is an important initial step towards understanding the dynamics of cluster-interactions in a temporal environment. Note that this paper, for the first time, systematically outlines a new avenue of research that one can practice post community detection.