# An influencer-based approach to understanding radical right viral tweets

arXiv: https://arxiv.org/abs/2109.07588 · HTML: https://mlsanity.com/paper/2109.07588/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Detecting East Asian Prejudice on Social Media](https://mlsanity.com/paper/2005.03909/) (2020)
- [Examining the consumption of radical content on YouTube](https://mlsanity.com/paper/2011.12843/) (2020)
- [Exploring limits to prediction in complex social systems](https://mlsanity.com/paper/1602.01013/) (2016)
- [Echo Chambers Exist! (But They're Full of Opposing Views)](https://mlsanity.com/paper/2001.11461/) (2020)
- [Analyzing Right-wing YouTube Channels: Hate, Violence and Discrimination](https://mlsanity.com/paper/1804.04096/) (2018)

## Abstract (arXiv)

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.