Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classification models rely on the textual representation of the utterance in question, prior work has demonstrated the importance of the conversational context in stance detection. In this work, we introduce TASTE – a multimodal architecture for stance detection that harmoniously fuses Transformer-based content embedding with unsupervised structural embedding. Through the fine-tuning of a pre-trained transformer and the amalgamation with social embedding via a Gated Residual Network (GRN) layer, our model adeptly captures the complex interplay between content and conversational structure in determining stance. TASTE achieves state-of-the-art results on common benchmarks, significantly outperforming an array of strong baselines. Comparative evaluations underscore the benefits of social grounding – emphasizing the criticality of concurrently harnessing both content and structure for enhanced stance detection.

Online communities develop unique characteristics, establish social norms, and exhibit distinct dynamics among their members. Activity in online communities often results in concrete “off-line” actions with a broad societal impact (e.g., political street protests and shifting norms related to sexual misconduct). While community dynamics, information diffusion, and online collaborations have been widely studied in the past two decades, quantitative studies that measure the effectiveness of online communities in promoting their agenda are scarce. In this work, we study the correspondence between the effectiveness of a community, measured by its success level in a competitive online campaign, and the underlying dynamics between its members. To this end, we de- fine a novel task: predicting the success level of online communities in Reddit’s r/place – a large-scale distributed experiment that required collaboration between community members. We consider an array of definitions for success level; each is geared toward different aspects of the collaborative achievement. We experiment with several hybrid models, combining various types of features. Our models significantly outperform all baseline models over all definitions of ‘success level’. Analysis of the results and the factors that contribute to the success of coordinated campaigns can provide a better understanding of the resilience or the vulnerability of communities to online social threats such as election interference or anti-science trends. We make all data used for this study publicly available for further research.

Online social platforms provide a bustling arena for information-sharing and for multiparty discussions.Various frameworks for dialogic discourse parsing were developed and used for the processing of discussions and for predicting the productivity of a dialogue.However, most of these frameworks are not suitable for the analysis of contentious discussions that are commonplace in many online platforms.A novel multi-label scheme for contentious dialog parsing was recently introduced by Zakharov et al. (2021).While the schema is well developed, the computational approach they provide is both naive and inefficient, as a different model (architecture) using a different representation of the input, is trained for each of the 31 tags in the annotation scheme.Moreover, all their models assume full knowledge of label collocations and context, which is unlikely in any realistic setting.In this work, we present a unified model for Non-Convergent Discourse Parsing that does not require any additional input other than the previous dialog utterances.We fine-tuned a RoBERTa backbone, combining embeddings of the utterance, the context and the labels through GRN layers and an asymmetric loss function.Overall, our model achieves results comparable with SOTA, without using label collocation and without training a unique architecture/model for each label.Our proposed architecture makes the labeling feasible at large scale, promoting the development of tools that deepen our understanding of discourse dynamics.

Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion -- we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.

With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge for the automatic detection of suicide risk. We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues, followed by a two-stage fine-tuning process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation, which is critical for real-time detection in the field. Moreover, the model performs well across genders and age groups.

Understanding collective decision making at a large-scale, and elucidating how community organization and community dynamics shape collective behavior are at the heart of social science research. In this work we study the behavior of thousands of communities with millions of active members. We define a novel task: predicting which community will undertake an unexpected, large-scale, distributed campaign. To this end, we develop a hybrid model, combining textual cues, community meta-data, and structural properties. We show how this multi-faceted model can accurately predict large-scale collective decision-making in a distributed environment. We demonstrate the applicability of our model through Reddit’s r/place – a large-scale online experiment in which millions of users, self-organized in thousands of communities, clashed and collaborated in an effort to realize their agenda.

Linguistic accommodation is the process in which speakers adjust their accent, diction, vocabulary, and other aspects of language according to the communication style of one another.Previous research has shown how linguistic accommodation correlates with gaps in the power and status of the speakers and the way it promotes approval and discussion efficiency.In this work, we provide a novel perspective on the phenomena, exploring its correlation with the open-mindedness of a speaker, rather than to her social status.We process thousands of unstructured argumentative discussions that took place in Reddit's Change My View (CMV) subreddit, demonstrating that open-mindedness relates to the assumed role of a speaker in different contexts.On the discussion level, we surprisingly find that discussions that reach agreement present lower levels of accommodation.

Sentence Compression is the task of generating a shorter, yet grammatical version of a given sentence, preserving the essence of the original sentence.This paper proposes a Black-Box Optimizer for Compression (B-BOC): given a black-box compression algorithm and assuming not all sentences need be compressed -find the best candidates for compression in order to maximize both compression rate and quality.Given a required compression ratio, we consider two scenarios: (i) single-sentence compression, and (ii) sentences-sequence compression.In the first scenario, our optimizer is trained to predict how well each sentence could be compressed while meeting the specified ratio requirement.In the latter, the desired compression ratio is applied to a sequence of sentences (e.g., a paragraph) as a whole, rather than on each individual sentence.To achieve that, we use B-BOC to assign an optimal compression ratio to each sentence, then cast it as a Knapsack problem, which we solve using bounded dynamic programming.We evaluate B-BOC on both scenarios on three datasets, demonstrating that our optimizer improves both accuracy and Rouge-F1-score compared to direct application of other compression algorithms.

Manual annotations are a prerequisite for many applications of machine learning.However, weaknesses in the annotation process itself are easy to overlook.In particular, scholars often choose what information to give to annotators without examining these decisions empirically.For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results.Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals.To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions.Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances.We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model's interpretable parameters shed light on the effects of each context.

"Man is by nature a political animal", as asserted by Aristotle. This political nature manifests itself in the data we produce and the traces we leave online. In this tutorial, we address a number of fundamental issues regarding mining of political data: What types of data could be considered political? What can we learn from such data? Can we use the data for prediction of political changes, etc? How can these prediction tasks be done efficiently? Can we use online socio-political data in order to get a better understanding of our political systems and of recent political changes? What are the pitfalls and inherent shortcomings of using online data for political analysis? In recent years, with the abundance of data, these questions, among others, have gained importance, especially in light of the global political turmoil and the upcoming 2016 US presidential election. We introduce relevant political science theory, describe the challenges within the framework of computational social science and present state of the art approaches bridging social network analysis, graph mining, and natural language processing.

Oren Tsur, Dan Calacci, David Lazer. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Work on authorship attribution has traditionally focused on long texts.In this work, we tackle the question of whether the author of a very short text can be successfully identified.We use Twitter as an experimental testbed.We introduce the concept of an author's unique "signature", and show that such signatures are typical of many authors when writing very short texts.We also present a new authorship attribution feature ("flexible patterns") and demonstrate a significant improvement over our baselines.Our results show that the author of a single tweet can be identified with good accuracy in an array of flavors of the authorship attribution task.

Current social media research mainly focuses on temporal trends of the information flow and on the topology of the social graph that facilitates the propagation of information. In this paper we study the effect of the content of the idea on the information propagation. We present an efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame. We show that a combination of content features with temporal and topological features minimizes prediction error.