Hate speech has long posed a serious problem for the integrity of digital platforms. Although significant progress has been made in identifying hate speech in its various forms, prevailing computational approaches have tended to consider it in isolation from the community-based contexts in which it spreads. In this paper, we propose a dynamic network framework to characterize hate communities, focusing on Twitter conversations related to COVID-19 in the United States and the Philippines. While average hate scores remain fairly consistent over time, hate communities grow increasingly organized in March, then slowly disperse in the succeeding months. This pattern is robust to fluctuations in the number of network clusters and average cluster size. Infodemiological analysis demonstrates that in both countries, the spread of hate speech around COVID-19 features similar reproduction rates as other COVID-19 information on Twitter, with spikes in hate speech generation at time points with highest community-level organization of hate speech. Identity analysis further reveals that hate in the US initially targets political figures, then grows predominantly racially charged; in the Philippines, targets of hate consistently remain political over time. Finally, we demonstrate that higher levels of community hate are consistently associated with smaller, more isolated, and highly hierarchical network clusters across both contexts. This suggests potentially shared structural conditions for the effective spread of hate speech in online communities even when functionally targeting distinct identity groups. Our findings bear theoretical and methodological implications for the scientific study of hate speech and understanding the pandemic’s broader societal impacts both online and offline.

仇恨言论长期以来一直困扰着数字平台的完整性。尽管在识别各种形式的仇恨言论方面已经取得了重大进展，但流行的计算方法倾向于将其与社区传播的仇恨环境隔离开来。在本文中，我们提出了一个动态网络框架来表征仇恨社区，重点关注与美国和菲律宾的COVID-19相关的Twitter对话。虽然平均仇恨分数会随着时间的推移保持相当一致，但仇恨社区在3月变得越来越有组织，然后在随后的几个月中逐渐消散。这种模式对于网络群集数量和平均群集大小的波动具有鲁棒性。信息流行病学分析表明，在这两个国家中，仇恨言论在Twitter上的传播速度与Twitter上其他COVID-19信息相似，其复制率与Twitter上的其他COVID-19信息相似，并且在仇恨言论的社区级别最高的时间点上，仇恨言论的产生激增。身份分析进一步表明，美国的仇恨最初是针对政治人物，然后主要是种族歧视。在菲律宾，随着时间的流逝，仇恨的目标始终保持政治性。最后，我们证明了较高的社区仇恨水平始终与跨这两种情况的较小，更孤立和高度分层的网络群集相关联。这表明即使在功能上针对不同的身份群体时，仇恨言论在在线社区中的有效传播也可能具有共享的结构性条件。