Variation in language use, shaped by speakers' sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions.For example, Chinese students discuss healthy eating with words like timing, regularity, and digestion, whereas Americans use vocabulary like balancing food groups and avoiding fat and sugar, reflecting distinct cultural models of nutrition (Banna et al., 2016).The computational study of these Sociocultural Linguistic Phenomena (SLP) has traditionally been done in NLP via tailored analyses of specific groups or topics, requiring specialized data collection and experimental operationalization-a process not well-suited to quick hypothesis exploration and prototyping.To address this, we propose constructing a "sandbox" designed for systematic and flexible sociolinguistic research.Using our method, we construct a demographically/topically split Reddit dataset, SPLITS!, validated by self-identification and by replicating several known SLPs from existing literature.We showcase the sandbox's utility with a scalable, two-stage process that filters large collections of potential SLPs (PSLPs) to surface the most promising candidates for deeper, qualitative investigation.

Health influencers play a growing role in shaping public beliefs 1 , yet their content is often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims.As a result, claimcentric verification methods struggle to capture the pragmatic meaning of influencer discourse.In this paper, we propose TAIGR (Takeaway Argumentation Inference with Grounded References), a structured framework designed to analyze influencer discourse, which operates in 3 stages: (1) identifying the core influencer recommendation-takeaway; (2) constructing an argumentation graph that captures influencer justification for the takeaway; (3) performing factor graph-based probabilistic inference to validate the takeaway.We evaluate TAIGR on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse's pragmatic and argumentative structure rather than treating transcripts as flat collections of claims.

Large language models (LLMs) can both evaluate and explain text quality; however, most existing evaluators operate as static classifiers and lack the ability to refine their reasoning through interaction.We propose an Iterative Alpha-Beta Learning framework that jointly trains two complementary 8B models: an Alpha (α) classifier that assesses pairwise story engagement, and a Beta (β) generator that produces structured, rubric-guided comparative explanations.The two models co-evolve within a closed feedback loop: α provides probabilistic preference signals to guide β's Direct Preference Optimization (DPO), while β's improved explanations are reintegrated to retrain α via a KL-based contrastive objective.This dual optimization enables mutual learning: α gains interpretability and robustness from β's textual rationales, while β acquires stronger alignment and discriminative precision from α's confidence deltas.Experiments on human-annotated storypair datasets (HANNA) show that the proposed system consistently outperforms strong singlemodel baselines in both accuracy and explanation quality across multiple iterative rounds 1 .

Finding evidence for human opinion and behavior at scale is a challenging task, often requiring an understanding of sophisticated thought patterns among vast online communities found on social media.For example, studying how gun ownership is related to the perception of Freedom, requires a retrieval system that can operate at scale over social media posts, while dealing with two key challenges: (1) identifying abstract concept instances, (2) which can be instantiated differently across different communities.To address these, we introduce CON-CEPTCARVE, an evidence retrieval framework that utilizes traditional retrievers and LLMs to dynamically characterize the search space during retrieval.Our experiments show that CON-CEPTCARVE surpasses traditional retrieval systems in finding evidence within a social media community.It also produces an interpretable representation of the evidence for that community, which we use to qualitatively analyze complex thought patterns that manifest differently across the communities.

Large Language Models (LLMs) not only have solved complex reasoning problems but also exhibit remarkable performance in tasks that require subjective decision-making.Existing studies suggest that LLM generations can convey subjectivity to some extent, yet exploring whether LLMs can account for individual-level subjectivity has not been sufficiently studied.In this paper, we characterize the subjectivity of individuals on social media and infer their moral judgments using LLMs.We propose a framework, SOLAR (Subjective GrOund with VaLue AbstRaction), that observes value conflicts and trade-offs in the user-generated texts to better represent subjective ground of individuals.Empirical results demonstrate that our framework enhances overall inference performance, with notable improvements for users with limited data and in controversial situations.Additionally, we qualitatively show that SO-LAR provides explanations about individuals' value preferences, which can further account for their judgments.

Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "public sentiment towards vaccination", "political discourse surrounding climate policies," etc. In this paper, we introduce a novel approach to uncovering latent themes in social media messaging. Recognizing the limitations of the traditional topic-level analysis, which tends to capture only overarching patterns, this study emphasizes the need for a finer-grained, theme-focused exploration. Traditional theme discovery methods typically involve manual processes and a human-in-the-loop approach. While valuable, these methods face challenges in scalability, consistency, and resource intensity in terms of time and cost. To address these challenges, we propose a machine-in-the-loop approach that leverages the advanced capabilities of large language models (LLMs). This approach facilitates a deeper investigation into the social media discourse, revealing a variety of themes with distinct characteristics and relevance. It provides a detailed understanding of underlying nuances and efficiently maps texts to these themes, enhancing our insight into social media messaging. To demonstrate our approach, we apply our framework to contentious topics, such as climate debate and vaccine debate. We use two publicly available datasets: (1) the climate campaigns dataset of 21k Facebook ads and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads. Our quantitative and qualitative analysis shows that our methodology yields more accurate and interpretable results compared to the baselines. Our results not only demonstrate the effectiveness of our approach in uncovering latent themes but also illuminate how these themes are tailored for demographic targeting in social media contexts. Additionally, our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.

Rajkumar Pujari, Dan Goldwasser. 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.

Political discourse on social media often contains similar language with opposing intended meanings.For example, the phrase thoughts and prayers, is used to express sympathy for mass shooting victims, as well as satirically criticize the lack of legislative action on gun control.Understanding such discourse fully by reading only the text is difficult.However, knowledge of the social context information makes it easier.We characterize the social context required to fully understand such ambiguous discourse, by grounding the text in real-world entities, actions, and attitudes.We propose two datasets that require an understanding of social context and benchmark them using large pre-trained language models and several novel structured models.We show that structured models, explicitly modeling social context, outperform larger models on both tasks, but still lag significantly behind human performance.Finally, we perform an extensive analysis, to obtain further insights into the language understanding challenges posed by our social grounding tasks.

Nikhil Mehta, Dan Goldwasser. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Maryam Davoodi, Dan Goldwasser. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns, especially those faced by energy industries. Our goal in this paper is to analyze how those industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. In this work, we propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook. Finally, we release our stance dataset, model, and set of themes related to climate campaigns for future work on opinion mining and the automatic detection of climate change stances.

Automated Program Repair (APR) improves soft-ware reliability by generating patches for a buggy program automatically. Recent APR techniques leverage deep learning (DL) to build models to learn to generate patches from existing patches and code corpora. While promising, DL-based APR techniques suffer from the abundant syntactically or semantically incorrect patches in the patch space. These patches often disobey the syntactic and semantic domain knowledge of source code and thus cannot be the correct patches to fix a bug. We propose a DL-based APR approach KNOD, which in-corporates domain knowledge to guide patch generation in a direct and comprehensive way. KNOD has two major novelties, including (1) a novel three-stage tree decoder, which directly generates Abstract Syntax Trees of patched code according to the inherent tree structure, and (2) a novel domain-rule distillation, which leverages syntactic and semantic rules and teacher-student distributions to explicitly inject the domain knowledge into the decoding procedure during both the training and inference phases. We evaluate KNOD on three widely-used benchmarks. KNOD fixes 72 bugs on the Defects4J v1.2, 25 bugs on the QuixBugs, and 50 bugs on the additional Defects4J v2.0 benchmarks, outperforming all existing APR tools.

Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, making this communication transparent is challenging, as the messaging is tightly coupled with its intended audience and often echoed by multiple stakeholders interested in advancing specific policies. Our goal in this paper is to take a first step towards understanding these highly decentralized settings. We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook and analyze how political campaigns use some kind of demographic targeting by location, gender, or age. Furthermore, we analyze the temporal dynamics of the political ads on election polls.

Decisions on state-level policies have a deep effect on many aspects of our everyday life, such as health-care and education access.However, there is little understanding of how these policies and decisions are being formed in the legislative process.We take a datadriven approach by decoding the impact of legislation on relevant stakeholders (e.g., teachers in education bills) to understand legislators' decision-making process and votes.We build a new dataset for multiple US states that interconnects multiple sources of data including bills, stakeholders, legislators, and money donors.Next, we develop a textual graphbased model to embed and analyze state bills.Our model predicts winners/losers of bills and then utilizes them to better determine the legislative body's vote breakdown according to demographic/ideological criteria, e.g., gender.

Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular.However, this rise has also enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs.Detecting it is an important and challenging problem to prevent large scale misinformation and maintain a healthy society.We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework.After embedding this information, we formulate inference operators which augment the graph edges by revealing unobserved interactions between its elements, such as similarity between documents' contents and users' engagement patterns.Our experiments over two challenging fake news detection tasks show that using inference operators leads to a better understanding of the social media framework enabling fake news spread, resulting in improved performance.

In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.

Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Automated attack discovery techniques, such as attacker synthesis or model-based fuzzing, provide powerful ways to ensure network protocols operate correctly and securely. Such techniques, in general, require a formal representation of the protocol, often in the form of a finite state machine (FSM). Unfortunately, many protocols are only described in English prose, and implementing even a simple network protocol as an FSM is time-consuming and prone to subtle logical errors. Automatically extracting protocol FSMs from documentation can significantly contribute to increased use of these techniques and result in more robust and secure protocol implementations.In this work we focus on attacker synthesis as a representative technique for protocol security, and on RFCs as a representative format for protocol prose description. Unlike other works that rely on rule-based approaches or use off-the-shelf NLP tools directly, we suggest a data-driven approach for extracting FSMs from RFC documents. Specifically, we use a hybrid approach consisting of three key steps: (1) large-scale word-representation learning for technical language, (2) focused zero-shot learning for mapping protocol text to a protocol-independent information language, and (3) rule-based mapping from protocol-independent information to a specific protocol FSM. We show the generalizability of our FSM extraction by using the RFCs for six different protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP. We demonstrate how automated extraction of an FSM from an RFC can be applied to the synthesis of attacks, with TCP and DCCP as case-studies. Our approach shows that it is possible to automate attacker synthesis against protocols by using textual specifications such as RFCs.

Expressive text encoders such as RNNs and Transformer Networks have been at the center of NLP models in recent work.Most of the effort has focused on sentence-level tasks, capturing the dependencies between words in a single sentence, or pairs of sentences.However, certain tasks, such as argumentation mining, require accounting for longer texts and complicated structural dependencies between them.Deep structured prediction is a general framework to combine the complementary strengths of expressive neural encoders and structured inference for highly structured domains.Nevertheless, when the need arises to go beyond sentences, most work relies on combining the output scores of independently trained classifiers.One of the main reasons for this is that constrained inference comes at a high computational cost.In this paper, we explore the use of randomized inference to alleviate this concern and show that we can efficiently leverage deep structured prediction and expressive neural encoders for a set of tasks involving complicated argumentative structures.

Politicians often have underlying agendas when reacting to events.Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity.In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns.In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events.These representations are contextualized by tweets, press releases, issues, news articles, and participating entities.Our model processes several documents at once and generates composed representations for multiple entities over several issues or events.Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.

Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions.The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity.However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities.In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and highquality annotated dataset of tweets written by US politicians.Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly.We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.

Understanding narrative text requires capturing characters' motivations, goals, and mental states.This paper proposes an Entity-based Narrative Graph (ENG) to model the internalstates of characters in a story.We explicitly model entities, their interactions and the context in which they appear, and learn rich representations for them.We experiment with different task-adaptive pre-training objectives, in-domain training, and symbolic inference to capture dependencies between different decisions in the output space.We evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.

Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.

While national politics often receive the spotlight, the overwhelming majority of legislation proposed, discussed, and enacted is done at the state level. Despite this fact, there is little awareness of the dynamics that lead to adopting these policies. In this paper, we take the first step towards a better understanding of these processes and the underlying dynamics that shape them, using data-driven methods. We build a new large-scale dataset, from multiple data sources, connecting state bills and legislator information, geographical information about their districts, and donations and donors' information. We suggest a novel task, predicting the legislative body's vote breakdown for a given bill, according to different criteria of interest, such as gender, rural-urban and ideological splits. Finally, we suggest a shared relational embedding model, representing the interactions between the text of the bill and the legislative context in which it is presented. Our experiments show that providing this context helps improve the prediction over strong text-based models.

Although yoga is a multi-component practice to hone the body and mind and be known to reduce anxiety and depression, there is still a gap in understanding people's emotional state related to yoga in social media. In this study, we investigate the causal relationship between practicing yoga and being happy by incorporating textual and temporal information of users using Granger causality. To find out causal features from the text, we measure two variables (i) Yoga activity level based on content analysis and (ii) Happiness level based on emotional state. To understand users' yoga activity, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by leveraging users' social and textual information. For measuring the emotional state of yoga users (target domain), we suggest a transfer learning approach to transfer knowledge from an attention-based neural network model trained on a source domain. Our experiment on Twitter dataset demonstrates that there are 1447 users where "yoga Granger-causes happiness".

Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language.In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents' languages.This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective.We further provide theoretical guarantee on the generalization error bound for the proposed framework.We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.

The ability to change a person's mind on a given issue depends both on the arguments they are presented with and on their underlying perspectives and biases on that issue.Predicting stance changes requires characterizing both aspects and the interaction between them, especially in realistic settings in which stance changes are very rare.In this paper, we suggest a modular learning approach, which decomposes the task into multiple modules, focusing on different aspects of the interaction between users, their beliefs, and the arguments they are exposed to.Our experiments show that our modular approach archives significantly better results compared to the end-to-end approach using BERT over the same inputs.

We describe two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing.The first model is a Locally Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Globally Autoencoding Parser (GAP) encoding the input into dependency trees as latent variables, with exact inference.Both models consist of two parts: an encoder enhanced by deep neural networks (DNN) that can utilize the contextual information to encode the input into latent variables, and a decoder which is a generative model able to reconstruct the input.Both LAP and GAP admit a unified structure with different loss functions for labeled and unlabeled data with shared parameters.We conducted experiments on WSJ and UD dependency parsing data sets, showing that our models can exploit the unlabeled data to improve the performance given a limited amount of labeled data, and outperform a previously proposed semi-supervised model.

In this paper, we suggest a minimally supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way. We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control, and abortion. We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.

In this work, we study collaborative online conversations.Such conversations are rich in content, constructive and motivated by a shared goal.Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions.To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks.These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread.We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Existing methods that incorporate interactive user feedback focus on historical datasets. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time. This limits real-time situational awareness, as streaming data that is incorrectly classified cannot be corrected immediately, permitting the possibility for important incoming data to be incorrectly classified as well. We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements. We computationally evaluate our classification model adapted to learn at interactive rates. Our results show that our approach outperforms state-of-the-art machine learning models. In addition, we integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system tailored for real-time situational awareness. To demonstrate our framework's effectiveness, we provide domain expert feedback from first responders who used the extended SMART 2.0 system.

Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.

Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Traditional knowledge graph embedding models learn continuous low-dimensional embedding of entities and relations. However, when applied to social networks, existing approaches do not consider the rich textual communications between users, which contains valuable information to describe social relationships. In this paper, we propose TransConv, a novel approach that incorporates textual interactions between pair of users to improve representation learning of both users and relationships. Our experiments on real social network data show TransConv learns better user and relationship embeddings compared to other state-of-theart knowledge graph embedding models. Moreover, the results illustrate that our model is more robust for sparse relationships where there are fewer examples.

Modeling script knowledge can be useful for a wide range of NLP tasks.Current statistical script learning approaches embed the events, such that their relationships are indicated by their similarity in the embedding.While intuitive, these approaches fall short of representing nuanced relations, needed for downstream tasks.In this paper, we suggest to view learning event embedding as a multi-relational problem, which allows us to capture different aspects of event pairs.We model a rich set of event relations, such as Cause and Contrast, derived from the Penn Discourse Tree Bank.We evaluate our model on three types of tasks, the popular Mutli-Choice Narrative Cloze and its variants, several multi-relational prediction tasks, and a related downstream task-implicit discourse sense classification.

Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task.In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks.We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents' social context.We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.

Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label.In this paper we focus on a popular class of learning problems, sequence prediction applied to several sentiment analysis tasks, and suggest a modular learning approach in which different sub-tasks are learned using separate functional modules, combined to perform the final task while sharing information.Our experiments show this approach helps constrain the learning process and can alleviate some of the supervision efforts.

In this paper we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors. ACE and ACE-KL provide insights in diagnosing which attributes significantly contribute to an anomaly by building a specialized linear model to locally approximate the anomaly score that a black-box model generates. We conducted experiments with these anomaly detection models to detect security anomalies on both synthetic data and real data. In particular, we evaluate performance on three public data sets: CERT insider threat, netflow logs, and Android malware. The experimental results are encouraging: our methods consistently identify the correct contributing feature in the synthetic data where ground truth is available; similarly, for real data sets, our methods point a security analyst in the direction of the underlying causes of an anomaly, including in one case leading to the discovery of previously overlooked network scanning activity. We have made our source code publicly available.

Statistical script learning is an effective way to acquire world knowledge which can be used for commonsense reasoning. Statistical script learning induces this knowledge by observing event sequences generated from texts. The learned model thus can predict subsequent events, given earlier events. Recent approaches rely on learning event embeddings which capture script knowledge. In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information. In addition to capturing the dependencies between subsequent events, our model can take into account higher level abstractions of the input event which help the model generalize better and account for the global context in which the event appears. We evaluated our model over three narrative cloze tasks, and showed that our model is competitive with the most recent state-of-the-art. We also show that our resulting embedding can be used as a strong representation for advanced semantic tasks such as discourse parsing and sentence semantic relatedness.

Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text.Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology.Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues.The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.

Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.

Phishing attacks continue to pose a major threat for computer system defenders, often forming the first step in a multi-stage attack. There have been great strides made in phishing detection; however, some phishing emails appear to pass through filters by making simple structural and semantic changes to the messages. We tackle this problem through the use of a machine learning classifier operating on a large corpus of phishing and legitimate emails. We design SAFe-PC (Semi-Automated Feature generation for Phish Classification), a system to extract features, elevating some to higher level features, that are meant to defeat common phishing email detection strategies. To evaluate SAFe-PC , we collect a large corpus of phishing emails from the central IT organization at a tier-1 university. The execution of SAFe-PC on the dataset exposes hitherto unknown insights on phishing campaigns directed at university users. SAFe-PC detects more than 70 percent of the emails that had eluded our production deployment of Sophos, a state-of-the-art email filtering tool. It also outperforms SpamAssassin, a commonly used email filtering tool. We also developed an online version of SAFe-PC, that can be incrementally retrained with new samples. Its detection performance improves with time as new samples are collected, while the time to retrain the classifier stays constant.

Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues.Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches.We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter.Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F 1 score by 21.52 points over a lexical baseline alone.

Fact-checking political discussions has become an essential clog in computational journalism. This task encompasses an important sub-task---identifying the set of statements with 'check-worthy' claims. Previous work has treated this as a simple text classification problem discounting the nuances involved in determining what makes statements check-worthy. We introduce a dataset of political debates from the 2016 US Presidential election campaign annotated using all major fact-checking media outlets and show that there is a need to model conversation context, debate dynamics and implicit world knowledge. We design a multi-classifier system TATHYA, that models latent groupings in data and improves state-of-art systems in detecting check-worthy statements by 19.5% in F1-score on a held-out test set, gaining primarily gaining in Recall.

In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems. Our NCRF-AE consists of two parts: an encoder which is a CRF model enhanced by deep neural networks, and a decoder which is a generative model trying to reconstruct the input. Our model has a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We developed a variation of the EM algorithm for optimizing both the encoder and the decoder simultaneously by decoupling their parameters. Our Experimental results over the Part-of-Speech (POS) tagging task on eight different languages, show that our model can outperform competitive systems in both supervised and semi-supervised scenarios.

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject's emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task.

During the 2016 United States presidential election, politicians have increasingly used Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events. Given the limited length of tweets and the scrutiny politicians face for what they choose or neglect to say, they must craft and time their tweets carefully. The content and delivery of these tweets is therefore highly indicative of a politician’s stances. We present a weakly supervised method for extracting how issues are framed and temporal activity patterns on Twitter for popular politicians and issues of the 2016 election. These behavioral components are combined into a global model which collectively infers the most likely stance and agreement patterns among politicians, with respective accuracies of 86.44% and 84.6% on average.

Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.

Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement will help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interaction with the MOOC open up avenues for studying student engagement at scale. In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues. Our first contribution is the abstraction of student engagement types using latent representations and using that in a probabilistic model to connect student behavior with course completion. We demonstrate that the latent formulation for engagement helps in predicting student survival across three MOOCs. Next, in order to initiate better instructor interventions, we need to be able to predict student survival early in the course. We demonstrate that we can predict student survival early in the course reliably using the latent model. Finally, we perform a closer quantitative analysis of user interaction with the MOOC and identify student activities that are good indicators for survival at different points in the course.

Instructor intervention in student discussion forums is a vital component in Massive Open Online Courses (MOOCs), where personalized interaction is limited. This paper introduces the problem of predicting instructor interventions in MOOC forums. We propose several prediction models designed to capture unique aspects of MOOCs, combining course information, forum structure and posts content. Our models abstract contents of individual posts of threads using latent categories, learned jointly with the binary intervention prediction problem. Experiments over data from two Coursera MOOCs demonstrate that incorporating the structure of threads into the learning problem leads to better predictive performance.

Understanding the actionable outcomes of a dialogue requires effectively modeling situational roles of dialogue participants, the structure of the dialogue and the relevance of each utterance to an eventual action.We develop a latent-variable model that can capture these notions and apply it in the context of courtroom dialogues, in which the objection speech act is used as binary supervision to drive the learning process.We demonstrate quantitatively and qualitatively that our model is able to uncover natural discourse structure from this distant supervision.

Maintaining and cultivating student engagement is a prerequisite for MOOCs to have broad educational impact. Understanding student engagement as a course progresses helps characterize student learning patterns and can aid in minimizing dropout rates, initiating instructor intervention. In this paper, we construct a probabilistic model connecting student behavior and class performance, formulating student engagement types as latent variables. We show that our model identifies course success indicators that can be used by instructors to initiate interventions and assist students.

We use visual analytics to explore participation in five MOOCs at the University of Maryland. In some of these courses, our analysis reveals interesting clustering patterns of student behavior. For other courses, visualizations provide "color" to help us better understand the range of student behavior.

Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples, requiring a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner using natural instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learning process. In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem instead of learning from labeled examples. This interpretation of machine learning is motivated by human learning processes, in which the learner is given a lesson describing the target concept directly, and a few instances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that gets feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. This approach alleviates the supervision burden of traditional machine learning by focusing on supplying the learner with only human-level task expertise for learning. We evaluate our approach by applying it to the rules of the Freecell solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also generalizes to previously unseen instructions. 1

We study the decision problem of disjunctive linear arithmetic over the reals from the perspective of computational geometry. We show that traversing the linear arrangement induced by the formula's predicates, rather than the DPLL(T) method of traversing the Boolean space, may have an advantage when the number of variables is smaller than the number of predicates (as it is indeed the case in the standard SMT-Lib benchmarks). We then continue by showing a branching heuristic that is based on approximating T-implications, based on a geometric analysis. We achieve modest improvement in run time comparing to the commonly used heuristic used by competitive solvers.