How can the birth and evolution of ideas and communities in a network be studied over time? We use a multimodal pipeline, consisting of network mapping, topic modeling, bridging centrality, and divergence to analyze Twitter data surrounding the COVID-19 pandemic. We use network mapping to detect accounts creating content surrounding COVID-19, then Latent Dirichlet Allocation to extract topics, and bridging centrality to identify topical and non-topical bridges, before examining the distribution of each topic and bridge over time and applying Jensen-Shannon divergence of topic distributions to show communities that are converging in their topical narratives.

中文翻译：

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文化融合：洞察 Twitter 上错误信息网络的行为

如何随着时间的推移研究网络中思想和社区的诞生和演变？我们使用由网络映射、主题建模、桥接中心性和发散组成的多模式管道来分析围绕 COVID-19 大流行的 Twitter 数据。我们使用网络映射来检测围绕 COVID-19 创建内容的帐户，然后使用潜在狄利克雷分配来提取主题，并桥接中心性以识别主题和非主题桥梁，然后检查每个主题和桥梁随时间的分布并应用 Jensen-Shannon主题分布的差异，以显示社区在其主题叙述中趋同。