Cultural Convergence: Insights into the Behavior of Misinformation Networks on Twitter
How can we study the birth and evolution of ideas in a network?
The above paper and presentation presents one answer to this question. With the goal of creating a quantitative measure of idea spread in a network, we developed 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 COVID related content, then LDA 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.
This work was completed at Graphika, Inc by myself and some of my very skilled colleagues. While some of us are no longer at Graphika, I remain interested in this work and am open to future collaborations to extend it.
