Cultural Convergence: Insights into the behavior of misinformation networks on TwitterDownload PDF

30 Jun 2020 (modified: 03 Jul 2024)Submitted to NLP-COVID-2020Readers: Everyone
Keywords: LDA, misinformation, cultural holes, network mapping, divergence, Twitter
TL;DR: We use a multimodal pipeline, consisting of network mapping, topic modeling, bridging centrality, and divergence to analyze Twitter data surrounding the COVID-19 pandemic.
Abstract: 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.
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