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1. Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions

   https://doi.org/10.18653/v1/2020.emnlp-main.605  

   Dutt, Ritam; Joshi, Rishabh; Rose, Carolyn (January 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   null (Ed.)

   The notion of face refers to the public self-image of an individual that emerges both from the individual’s own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings. 

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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. 

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3. Demo: An Exploration of LLM-Guided Conversation in Reminiscence Therapy

   Duan, J; Zhao, X; Zhang, Z; Ko, E; Boddy, L; Wang, C; Li, T; Rasgon, A; Hong, J; Lee, M K; et al (December 2024, Gen AI f or Health Workshop @ NeurIPS 2024, Vancouver.)

   Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations—where LLMs direct the discourse and steer the conversation’s objectives—remains largely untapped. In this study, we provide an exploration of the LLM-guided conversation paradigm. Specifically, we first characterize LLM-guided conversation into three fundamental properties: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GUIDELLM as a general framework for LLM-guided conversation. We then implement an autobiography interviewing environment as one of the demonstrations of GuideLLM, which is a common practice in Reminiscence Therapy. In this environment, various techniques are integrated with GUIDELLM to enhance the autonomy of LLMs, such as Verbalized Interview Protocol (VIP) and Memory Graph Extrapolation (MGE) for goal navigation, and therapy strategies for empathetic engagement. We compare GUIDELLM with baseline LLMs, such as GPT-4-turbo and GPT-4o, from the perspective of interviewing quality, conversation quality, and autobiography generation quality. Experimental results encompassing both LLM-as-a-judge evaluations and human subject experiments involving 45 participants indicate that GUIDELLM significantly outperforms baseline LLMs in the autobiography interviewing task. 

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4. M2P2: Multimodal Persuasion Prediction using Adaptive Fusion

   https://doi.org/10.1109/TMM.2021.3134168  

   Bai, Chongyang; Chen, Haipeng; Kumar, Srijan; Leskovec, Jure; Subrahmanian, V.S. (December 2021, IEEE Transactions on Multimedia)

   Identifying persuasive speakers in an adversarial environment is a critical task. In a national election, politicians would like to have persuasive speakers campaign on their behalf. When a company faces adverse publicity, they would like to engage persuasive advocates for their position in the presence of adversaries who are critical of them. Debates represent a common platform for these forms of adversarial persuasion. This paper solves two problems: the Debate Outcome Prediction (DOP) problem predicts who wins a debate while the Intensity of Persuasion Prediction (IPP) problem predicts the change in the number of votes before and after a speaker speaks. Though DOP has been previously studied, we are the first to study IPP. Past studies on DOP fail to leverage two important aspects of multimodal data: 1) multiple modalities are often semantically aligned, and 2) different modalities may provide diverse information for prediction. Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem. To leverage the alignment of different modalities while maintaining the diversity of the cues they provide, M2P2 devises a novel adaptive fusion learning framework which fuses embeddings obtained from two modules -- an alignment module that extracts shared information between modalities and a heterogeneity module that learns the weights of different modalities with guidance from three separately trained unimodal reference models. We test M2P2 on the popular IQ2US dataset designed for DOP. We also introduce a new dataset called QPS (from Qipashuo, a popular Chinese debate TV show) for IPP - we plan to release this dataset when the paper is published. M2P2 significantly outperforms 3 recent baselines on both datasets. 

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5. Timeliness Matters: Leveraging Reinforcement Learning on Social Media Data to Prioritize High-Risk Conversations for Promoting Youth Online Safety

   https://doi.org/10.1609/icwsm.v19i1.35802  

   Alsoubai, Ashwaq; Park, Jinkyung Katie; Stringhini, Gianluca; Ma, Meiyi; De_Choudhury, Munmun; Wisniewski, Pamela J (June 2025, Proceedings of the International AAAI Conference on Web and Social Media)

   Ensuring the online safety of youth has motivated research towards the development of machine learning (ML) methods capable of accurately detecting social media risks after-the-fact. However, for these detection models to be effective, they must proactively identify high-risk scenarios (e.g., sexual solicitations, cyberbullying) to mitigate harm. This `real-time' responsiveness is a recognized challenge within the risk detection literature. Therefore, this paper presents a novel two-level framework that first uses reinforcement learning to identify conversation stop points to prioritize messages for evaluation. Then, we optimize state-of-the-art deep learning models to accurately categorize risk priority (low, high). We apply this framework to a time-based simulation using a rich dataset of 23K private conversations with over 7 million messages donated by 194 youth (ages 13-21). We conducted an experiment comparing our new approach to a traditional conversation-level baseline. We found that the timeliness of conversations significantly improved from over 2 hours to approximately 16 minutes with only a slight reduction in accuracy (0.88 to 0.84). This study advances real-time detection approaches for social media data and provides a benchmark for future training reinforcement learning that prioritizes the timeliness of classifying high-risk conversations. 

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