论文标题
这一定是一个地方：预测在线社区参与大规模分布的活动
This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign
论文作者
论文摘要
在大规模的情况下了解集体决策，并阐明社区组织和社区动态如何塑造集体行为是社会科学研究的核心。在这项工作中，我们研究了成千上万有活跃成员的社区的行为。我们定义了一项新颖的任务：预测哪个社区将进行意外的大规模分布活动。 为此，我们开发了一个混合模型，结合了文本提示，社区元数据和结构属性。我们展示了这种多面模型如何在分布式环境中准确预测大规模的集体决策。我们通过Reddit的R/Place（一个大规模的在线实验）展示了我们的模型的适用性，其中数百万用户在成千上万的社区中进行自组织，并进行了冲突和合作，以实现他们的议程。 我们的杂种模型的高F1预测评分为0.826。我们发现，粗元功能对于预测准确性与细颗粒的文本提示一样重要，而显式结构特征则具有较小的作用。解释我们的模型，我们提供并支持有关参与\ r/place实验社区的独特特征的各种社会见解。 我们的结果和分析阐明了推动集体行为的复杂社会动态以及推动用户协调的因素。 \ rp〜实验的规模和独特条件表明，我们的发现可能适用于更广泛的环境，例如在线行动主义，（反对）仇恨言论的传播和减少政治两极分化。该模型的更广泛适用性通过对Wallstreetbets社区的广泛分析，在R/Place中的作用以及四年后的2021年GameStop Short Squeeze运动中的作用来证明。
Understanding collective decision making at a large-scale, and elucidating how community organization and community dynamics shape collective behavior are at the heart of social science research. In this work we study the behavior of thousands of communities with millions of active members. We define a novel task: predicting which community will undertake an unexpected, large-scale, distributed campaign. To this end, we develop a hybrid model, combining textual cues, community meta-data, and structural properties. We show how this multi-faceted model can accurately predict large-scale collective decision-making in a distributed environment. We demonstrate the applicability of our model through Reddit's r/place - a large-scale online experiment in which millions of users, self-organized in thousands of communities, clashed and collaborated in an effort to realize their agenda. Our hybrid model achieves a high F1 prediction score of 0.826. We find that coarse meta-features are as important for prediction accuracy as fine-grained textual cues, while explicit structural features play a smaller role. Interpreting our model, we provide and support various social insights about the unique characteristics of the communities that participated in the \r/place experiment. Our results and analysis shed light on the complex social dynamics that drive collective behavior, and on the factors that propel user coordination. The scale and the unique conditions of the \rp~experiment suggest that our findings may apply in broader contexts, such as online activism, (countering) the spread of hate speech and reducing political polarization. The broader applicability of the model is demonstrated through an extensive analysis of the WallStreetBets community, their role in r/place and four years later, in the GameStop short squeeze campaign of 2021.
