Tootfinder

Queuing for Civility: Regulating Emotions and Reducing Toxicity in Digital Discourse
Akriti Verma, Shama Islam, Valeh Moghaddam, Adnan Anwar
https://arxiv.org/abs/2509.00696 htt…

Queuing for Civility: Regulating Emotions and Reducing Toxicity in Digital Discourse
The pervasiveness of online toxicity, including hate speech and trolling, disrupts digital interactions and online well-being. Previous research has mainly focused on post-hoc moderation, overlooking the real-time emotional dynamics of online conversations and the impact of users' emotions on others. This paper presents a graph-based framework to identify the need for emotion regulation within online conversations. This framework promotes self-reflection to manage emotional responses and encour…

Can AI agents understand spoken conversations about data visualizations in online meetings?
Rizul Sharma, Tianyu Jiang, Seokki Lee, Jillian Aurisano
https://arxiv.org/abs/2510.00245

Can AI agents understand spoken conversations about data visualizations in online meetings?
In this short paper, we present work evaluating an AI agent's understanding of spoken conversations about data visualizations in an online meeting scenario. There is growing interest in the development of AI-assistants that support meetings, such as by providing assistance with tasks or summarizing a discussion. The quality of this support depends on a model that understands the conversational dialogue. To evaluate this understanding, we introduce a dual-axis testing framework for diagnosing th…

Influence of the majority group on individual judgments in online spontaneous conversations
Diletta Goglia, Davide Vega, Alessio Gandelli
https://arxiv.org/abs/2509.21092 https:…

Influence of the majority group on individual judgments in online spontaneous conversations
This study investigates how the majority group influences individual judgment formation and expression in anonymous, spontaneous online conversations. Drawing on theories of social conformity and anti-conformity, we analyze everyday dilemmas discussed on social media. First, using digital traces to operationalize judgments, we measure the conversations' disagreement and apply Bayesian regression to capture shifts of judgments formation before and after the group's exposure. Then we analyze chan…

"I don't have time for checking stuff, Liam. My friends online show me the truth. There are too many inmigrants"
Mary, a nurse whose frustrations spill over into family life. As tensions rise around the dinner table, the film explores how personal struggles can shape beliefs and conversations.
https://www.youtube…

Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversations
Leen Almajed, Abeer ALdayel
https://arxiv.org/abs/2509.10184 https://

Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversations
In emotionally supportive conversations, well-intended positivity can sometimes misfire, leading to responses that feel dismissive, minimizing, or unrealistically optimistic. We examine this phenomenon of incongruent positivity as miscalibrated expressions of positive support in both human and LLM generated responses. To this end, we collected real user-assistant dialogues from Reddit across a range of emotional intensities and generated additional responses using large language models for the …

Simulating Online Social Media Conversations on Controversial Topics Using AI Agents Calibrated on Real-World Data
Elisa Composta, Nicolo' Fontana, Francesco Corso, Francesco Pierri
https://arxiv.org/abs/2509.18985

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation
Wentian Zhu, Zhen Xiang, Wei Niu, Le Guan
https://arxiv.org/abs/2510.10271 https://

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation
Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the fine-tuning process of Large Language Models (LLMs). Serving as metadata of training data, these tokens play a crucial role in instructing LLMs to generate coherent and context-aware responses. We demonstrate that special tokens can be exploited to construct four attack primitives, with which malicious users can reliably bypass the internal safety ali…

The Chilling: Identifying Strategic Antisocial Behavior Online and Examining the Impact on Journalists
Yian Wang, Mukhilshankar Umashankar, Eshwar CHandrasekharan, Hari Sundaram
https://arxiv.org/abs/2508.15061

The Chilling: Identifying Strategic Antisocial Behavior Online and Examining the Impact on Journalists
On social platforms like Twitter, strategic targeted attacks are becoming increasingly common, especially against vulnerable groups such as female journalists. Two key challenges in identifying strategic online behavior are the complex structure of online conversations and the hidden nature of potential strategies that drive user behavior. To address these, we develop a new tree structured Transformer model that categorizes replies based on their hierarchical conversation structures. Extensive …

Identifying Constructive Conflict in Online Discussions through Controversial yet Toxicity Resilient Posts
Ozgur Can Seckin, Bao Tran Truong, Alessandro Flammini, Filippo Menczer
https://arxiv.org/abs/2509.18303

Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
Yisha Wu (Mia), Cen (Mia), Zhao, Yuanpei Cao, Xiaoqing Su, Yashar Mehdad, Mindy Ji, Claire Na Cheng
https://arxiv.org/abs/2510.06677

Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents' context-switching effort and redundant review. Our approach combines a fine-tuned Mixtral-8x7B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. Agent edits refine the online notes generation and regularly inform offline model retraining, closing the agent edits feedba…

Fostering Collective Discourse: A Distributed Role-Based Approach to Online News Commenting
Yoojin Hong, Yersultan Doszhan, Joseph Seering
https://arxiv.org/abs/2510.02766 https…

Fostering Collective Discourse: A Distributed Role-Based Approach to Online News Commenting
Current news commenting systems are designed based on implicitly individualistic assumptions, where discussion is the result of a series of disconnected opinions. This often results in fragmented and polarized conversations that fail to represent the spectrum of public discourse. In this work, we develop a news commenting system where users take on distributed roles to collaboratively structure the comments to encourage a connected, balanced discussion space. Through a within-subject, mixed-met…

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Matus Krajcovic, Peter Demcak, Eduard Kuric
https://arxiv.org/abs/2508.02376

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Embodied conversational agents (ECAs) are increasingly more realistic and capable of dynamic conversations. In online surveys, anthropomorphic agents could help address issues like careless responding and satisficing, which originate from the lack of personal engagement and perceived accountability. However, there is a lack of understanding of how ECAs in user experience research may affect participant engagement, satisfaction, and the quality of responses. As a proof of concept, we propose an …