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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. 

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

   Zhang, Justine; Chang, Jonathan P.; Danescu-Niculescu-Mizil, Cristian; Dixon, Lucas; Hua, Yiqing; Tahin, Nithum; Taraborelli, Dario. (January 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. 

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3. Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support

   https://doi.org/10.1145/3555095  

   Schluger, Charlotte; Chang, Jonathan P.; Danescu-Niculescu-Mizil, Cristian; Levy, Karen (November 2022, Proceedings of the ACM on Human-Computer Interaction)

   To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. Thisreactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms---such as proactively discouraging the emergence of antisocial behavior rather than reacting to it---and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow. 

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4. Prosocial Norm Emergence in Multi-agent Systems

   https://doi.org/10.1145/3540202  

   Mashayekhi, Mehdi; Ajmeri, Nirav; List, George F.; Singh, Munindar P. (June 2022, ACM Transactions on Autonomous and Adaptive Systems)

   Multi-agent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multi-agent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multi-agent system can be reflected in the social norms among its members. It is well recognized that the norms that arise in society are not always beneficial to its members. We focus on prosocial norms, which help achieve positive outcomes for society and often provide guidance to agents to act in a manner that takes into account the welfare of others. Specifically, we propose Cha, a framework for the emergence of prosocial norms. Unlike previous norm emergence approaches, Cha supports continual change to a system (agents may enter and leave) and dynamism (norms may change when the environment changes). Importantly, Cha agents incorporate prosocial decision-making based on inequity aversion theory, reflecting an intuition of guilt arising from being antisocial. In this manner, Cha brings together two important themes in prosociality: decision-making by individuals and fairness of system-level outcomes. We demonstrate via simulation that Cha can improve aggregate societal gains and fairness of outcomes. 

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5. Analyzing and Characterizing User Intent in Information-seeking Conversations

   https://doi.org/10.1145/3209978.3210124  

   Qu, Chen; Yang, Liu; Croft, W. Bruce; Trippas, Johanne R.; Zhang, Yongfeng; Qiu, Minghui (July 2018, Proceedings of the 41st International ACM SIGIR Conference onResearch and Development in Information Retrieval)

   Understanding and characterizing how people interact in information-seeking conversations will be a crucial component in developing effective conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled conversation dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intents on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models. 

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