论文标题
文化融合：对Twitter上错误信息网络行为的见解
Cultural Convergence: Insights into the behavior of misinformation networks on Twitter
论文作者
论文摘要
如何随着时间的推移研究网络中思想和社区的出生和发展？我们使用由网络映射，主题建模，桥接中心性和差异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的内容，然后在围绕covid-19的内容，然后进行潜在的dirichlet分配来提取主题，并桥接中心性，以识别局部和非流行桥梁，然后再检查每个主题的分布和随时间的分布并随着时间的推移桥梁的分布并应用詹森·桑农（Jensen-Shannon Shannon）分布，以展示主题分布的社区，这些社区在其局部官方官方官方官方官方官方范围内都得到了转化。
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.
