Millions of people use online social networks to reinforce their sense of belonging, for example by giving and asking for feedback as a form of social validation and self-recognition. It is common to observe disagreement among people beliefs and points of view when expressing this feedback. Modeling and analyzing such interactions is crucial to understand social phenomena that happen when people face different opinions while expressing and discussing their values. In this work, we study a Reddit community in which people participate to judge or be judged with respect to some behavior, as it represents a valuable source to study how users express judgments online. We model threads of this community as complex networks of user interactions growing in time, and we analyze the evolution of their structural properties. We show that the evolution of Reddit networks differ from other real social networks, despite falling in the same category. This happens because their global clustering coefficient is extremely small and the average shortest path length increases over time. Such properties reveal how users discuss in threads, i.e. with mostly one other user and often by a single message. We strengthen such result by analyzing the role that disagreement and reciprocity play in such conversations. We also show that Reddit thread's evolution over time is governed by two subgraphs growing at different speeds. We discover that, in the studied community, the difference of such speed is higher than in other communities because of the user guidelines enforcing specific user interactions. Finally, we interpret the obtained results on user behavior drawing back to Social Judgment Theory.
Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit
DIMES Dept., University of Calabria Italy；IT University of Copenhagen\ Centre for AI Denmark；DIMES Dept., University of Calabria；IT University of Copenhagen\ Centre for AI
Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forum
Institute of Physics Belgrade, University of Belgrade（贝尔格莱德物理研究所，贝尔格莱德大学）；Faculty of Philosophy, University of Novi Sad（诺维萨德大学哲学学院）；Vinča Institute of Nuclear Sciences, University of Belgrade（温查核科学研究所，贝尔格莱德大学）
Action selection in growing state spaces: Control of Network Structure Growth
Donders Institute for Brain, Cognition and Behaviour Radboud University（拉波德大学唐德斯大脑、认知与行为研究所）；Department of Information and Communication Technologies Universitat Pompeu Fabra（庞培法布拉大学信息与通信技术系）
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text
Netaji Subhas Institute of Technology（内塔吉·苏巴斯理工学院）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
人们越来越有兴趣了解人类如何发起和进行对话。会话的情感理解关注的是说话者如何利用情感对情景和彼此做出反应的问题。在CL-Aff共享任务中，组织者发布了Get it #OffMyChest数据集，其中包含来自休闲和忏悔对话的Reddit评论，标记为披露和保密特征。在本文中，我们介绍了一个预测集成模型，利用微调的上下文词嵌入，罗伯特和阿尔伯特。我们表明，我们的模型在所有考虑的指标优于基础模型，实现了$3\%$的F1分数的改善。我们进一步进行统计分析，并概述对给定数据集的更深入见解，同时为数据集提供新的影响特征。
