At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in-vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02% on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings on average, of posts by 5.15% and of comments by 37.4%. Compared to the control group, the probability of reaching a high rating (>=2000) for posts is increased by 24.6% when posts receive the positive treatment and for comments is decreased by 46.6% when comments receive the negative treatment.
Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）
A Decade of News Forum Interactions: Threaded Conversations, Signed Votes, and Topical Tags
Barcelona Supercomputing Center (BSC-CNS)（巴塞罗那超级计算中心）；Department of Engineering, Universitat Pompeu Fabra（庞培法布拉大学工程学院）；Ethical Technologies and Connectivity for Humanity Research Center, Universitat Oberta de Catalunya（加泰罗尼亚开放大学人类伦理技术与连接研究中心）
Counterspeech for Mitigating the Influence of Media Bias: Comparing Human and LLM-Generated Responses
The Chinese University of Hong Kong, Hong Kong, China（香港中文大学）；MoE Key Laboratory of High Confidence Software Technologies（高可信软件技术国家重点实验室）；Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））
Hunting for Troll Comments in News Community Forums
Sofia University（索非亚大学）；Institute for Computational Linguistics（计算语言学研究所）；Heidelberg University（海德堡大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad bin Khalifa University（哈马德·本·哈利法大学）
MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Division of Emerging Interdisciplinary Areas, Hong Kong University of Science and Technology（香港科技大学新兴交叉学科 division）
Negative news posts are less prevalent and generate lower user engagement than non-negative news posts across six countries
Center for Excellence in Social Science, University of Warsaw（社会科学研究卓越中心，华沙大学）；Department of Communication, University of California, Davis（传播学系，加州大学戴维斯分校）；Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw（数学与计算建模跨学科中心，华沙大学）
