Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Scientists Discover Anti-Maskers Are Really Good At Science! Uh-oh.
MIT did a study on Covid Skeptics and guess what? They found out we're better at science, data analytics and communication than they are!
Link to study: [arxiv.org]

Dear God - The Study is so complex. How much did that 'study' cost? Who paid for that?

[arxiv.org]

One small part of 18-pages -

3.1.3 Network analysis. Finally, to analyze the users participating in these discussions, we constructed a network graph: nodes
were users who appeared in our dataset, and edges were drawn
between pairs of nodes if a user mentioned, replied to, retweeted,
or quote-tweeted another user. The resultant graph consisted of almost 400,000 nodes and over 583,000 edges, with an average degree
of 2.9. To produce a denser network structure, we calculated a histogram of node degrees and identified that two-thirds of the nodes
were degree 1. We then computed subgraphs, filtering for nodes
with a minimum degree of 2 through 10 and found that degree 5
offered us a good balance between focusing on the most influential
actors in the network, without losing smaller yet salient communities. This step yielded a subgraph of over 28,000 nodes and 104,000
edges, with an average degree of 7.3. We detected communities on
this network using the Louvain method [12]. Of the 2,573 different
communities detected by this algorithm, we primarily focus on the
top 10 largest communities which account for 72% of nodes, 80% of
edges, and 30% of visualizations.