Cultural Convergence: Insights into the behavior of misinformation networks on Twitter
Liz McQuillan, Erin McAweeney, Alicia Bargar, Alex Ruch
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.
cs.SI, cs.CL, physics.soc-ph, H.1.2; H.4.3; I.2.1; I.2.6; I.2.7; I.5