An influencer-based approach to understanding radical right viral tweets
This addresses the challenge of countering divisive online content for social media platforms and researchers, but it is incremental as it builds on prior work by focusing on viral factors.
The study tackled the problem of understanding what factors cause radical right content to go viral on social media, using a new dataset (ROT) of 35 influencers and over 50,000 entries, and found that influencer-level structure and factors like follower count, content type, length, toxicity, and retweet requests are crucial.
Radical right influencers routinely use social media to spread highly divisive, disruptive and anti-democratic messages. Assessing and countering the challenge that such content poses is crucial for ensuring that online spaces remain open, safe and accessible. Previous work has paid little attention to understanding factors associated with radical right content that goes viral. We investigate this issue with a new dataset ROT which provides insight into the content, engagement and followership of a set of 35 radical right influencers. It includes over 50,000 original entries and over 40 million retweets, quotes, replies and mentions. We use a multilevel model to measure engagement with tweets, which are nested in each influencer. We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets. We make ROT available for other researchers to use.