Online communities develop unique characteristics, establish social norms,
and exhibit distinct dynamics among their members. Activity in online
communities often results in concrete ``off-line'' actions with a broad
societal impact (e.g., political street protests and norms related to sexual
misconduct). While community dynamics, information diffusion, and online
collaborations have been widely studied in the past two decades, quantitative
studies that measure the effectiveness of online communities in promoting their
agenda are scarce. In this work, we study the correspondence between the
effectiveness of a community, measured by its success level in a competitive
online campaign, and the underlying dynamics between its members. To this end,
we define a novel task: predicting the success level of online communities in
Reddit's r/place - a large-scale distributed experiment that required
collaboration between community members. We consider an array of definitions
for success level; each is geared toward different aspects of collaborative
achievement. We experiment with several hybrid models, combining various types
of features. Our models significantly outperform all baseline models over all
definitions of `success level'. Analysis of the results and the factors that
contribute to the success of coordinated campaigns can provide a better
understanding of the resilience or the vulnerability of communities to online
social threats such as election interference or anti-science trends. We make
all data used for this study publicly available for further research.

在线社区的效力与成员间的动态之间的对应关系的研究，以 Reddit 的 r/place 为例，分析协同活动成功与不同因素之间的关系，为了解社区面对选举干扰或反科学趋势等在线社交威胁的韧性或脆弱性提供更好的理解。

大规模协作活动中预测社区成功

With Flying Colors: Predicting Community Success in Large-scale  Collaborative Campaigns

Counterspeech offers direct rebuttals to hateful speech by challenging
perpetrators of hate and showing support to targets of abuse. It provides a
promising alternative to more contentious measures, such as content moderation
and deplatforming, by contributing a greater amount of positive online speech
rather than attempting to mitigate harmful content through removal. Advances in
the development of large language models mean that the process of producing
counterspeech could be made more efficient by automating its generation, which
would enable large-scale online campaigns. However, we currently lack a
systematic understanding of several important factors relating to the efficacy
of counterspeech for hate mitigation, such as which types of counterspeech are
most effective, what are the optimal conditions for implementation, and which
specific effects of hate it can best ameliorate. This paper aims to fill this
gap by systematically reviewing counterspeech research in the social sciences
and comparing methodologies and findings with computer science efforts in
automatic counterspeech generation. By taking this multi-disciplinary view, we
identify promising future directions in both fields.

用计算机科学的方法对社会科学领域中的对抗性言论研究进行系统性回顾，比较方法和结果，以填补目前在对抗性言论效用方面缺乏系统性理解的空白，从而识别出两个领域的有前途的未来方向。