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1. Conversations Gone Awry: Detecting Early Signs of Conversational Failure

   Zhang, Justine ; Chang, Jonathan P. ; Dixon, Lucas ; Hua, Yiqing ; Tahin, Nithum ; Taraborelli, Dario. ( July 2018 , Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics)

   One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged. To this end, we develop a framework for capturing pragmatic devices---such as politeness strategies and rhetorical prompts---used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions.
2. Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations

   https://doi.org/10.1145/3442381.3450122  

   Bao, Jiajun ; Wu, Junjie ; Zhang, Yiming ; Chandrasekharan, Eshwar ; Jurgens, David ( April 2021 , The Web Conference)

   Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detecting and reducing antisocial behavior. Yet we know little about positive outcomes in online conversations and how to increase them—is a prosocial outcome simply the lack of antisocial behavior or something more? Here, we examine how conversational features lead to prosocial outcomes within online discussions. We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Using a corpus of 26M Reddit conversations, we show that these outcomes can be forecasted from the initial comment of an online conversation, with the best model providing a relative 24% improvement over human forecasting performance at ranking conversations for predicted outcome. Our results indicate that platforms can use these early cues in their algorithmic ranking of early conversations to prioritize better outcomes.
3. Forecasting the presence and intensity of hostility on Instagram using linguistic and social features

   Liu, P. ; Guberman, J. ; Hemphill, L. ; Culotta, A. ( January 2018 , Proceedings of the 12th International Conference on Web and Social Media)

   Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities. While prior work has proposed automated methods to identify hostile comments in online discussions, these methods work retrospectively on comments that have already been posted, making it difficult to intervene before an interaction escalates. In this paper we instead consider the problem of forecasting future hostilities in online discussions, which we decompose into two tasks: (1) given an initial sequence of non-hostile comments in a discussion, predict whether some future comment will contain hostility; and (2) given the first hostile comment in a discussion, predict whether this will lead to an escalation of hostility in subsequent comments. Thus, we aim to forecast both the presence and intensity of hostile comments based on linguistic and social features from earlier comments. To evaluate our approach, we introduce a corpus of over 30K annotated Instagram comments from over 1,100 posts. Our approach is able to predict the appearance of a hostile comment on an Instagram post ten or more hours in the future with an AUC of .82 (task 1), and can furthermore distinguishmore »between high and low levels of future hostility with an AUC of .91 (task 2).« less
4. Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online Discussions

   https://doi.org/10.1145/3366423.3380273  

   Chang, Jonathan P. ; Cheng, Justin ; Danescu-Niculescu-Mizil, Cristian ( January 2020 , Proceedings of WWW)

   Discourse involves two perspectives: a person’s intention in making an utterance and others’ perception of that utterance. The misalignment between these perspectives can lead to undesirable outcomes, such as misunderstandings, low productivity and even overt strife. In this work, we present a computational framework for exploring and comparing both perspectives in online public discussions. We combine logged data about public comments on Facebook with a survey of over 16,000 people about their intentions in writing these comments or about their perceptions of comments that others had written. Unlike previous studies of online discussions that have largely relied on third-party labels to quantify properties such as sentiment and subjectivity, our approach also directly captures what the speakers actually intended when writing their comments. In particular, our analysis focuses on judgments of whether a comment is stating a fact or an opinion, since these concepts were shown to be often confused. We show that intentions and perceptions diverge in consequential ways. People are more likely to perceive opinions than to intend them, and linguistic cues that signal how an utterance is intended can differ from those that signal how it will be perceived. Further, this misalignment between intentions and perceptions can bemore »linked to the future health of a conversation: when a comment whose author intended to share a fact is misperceived as sharing an opinion, the subsequent conversation is more likely to derail into uncivil behavior than when the comment is perceived as intended. Altogether, these findings may inform the design of discussion platforms that better promote positive interactions.« less
5. WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community

   Hua, Yiqing ; Danescu-Niculescu-Mizil, Cristian ; Taraborelli, Dario ; Thain, Nithum ; Sorensen, Jeffery ; Dixon, Lucas ( January 2018 , Proceedings of the Conference on Empirical Methods in Natural Language Processing)

   We present a corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations—including not only comments and replies, but also their modifications, deletions and restorations—this data offers an unprecedented view of online conversation. This level of detail supports new research questions pertaining to the process (and challenges) of large-scale online collaboration. We illustrate the corpus’ potential with two case studies that highlight new perspectives on earlier work. First, we explore how a person’s conversational behavior depends on how they relate to the discussion’s venue. Second, we show that community moderation of toxic behavior happens at a higher rate than previously estimated. Finally the reconstruction framework is designed to be language agnostic, and we show that it can extract high quality conversational data in both Chinese and English.