Cultural Convergence: Insights into the behavior of misinformation networks on Twitter
This work addresses the problem of understanding misinformation spread for researchers and policymakers, but it is incremental as it applies existing methods to a specific dataset.
The study analyzed Twitter data during the COVID-19 pandemic to track the evolution of misinformation communities, using network mapping, topic modeling, and divergence metrics to show how topical narratives converge over time.
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.