mlsanityData to 7 Oct 2026

Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit

arXiv 2310.12186 ↗First posted 18 Oct 2023cs.SI cs.AIcited by 5

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Abstract (arXiv)

Fringe communities promoting conspiracy theories and extremist ideologies have thrived on mainstream platforms, raising questions about the mechanisms driving their growth. Here, we hypothesize and study a possible mechanism: new members may be recruited through fringe-interactions: the exchange of comments between members and non-members of fringe communities. We apply text-based causal inference techniques to study the impact of fringe-interactions on the growth of three prominent fringe communities on Reddit: r/Incel, r/GenderCritical, and r/The_Donald. Our results indicate that fringe-interactions attract new members to fringe communities. Users who receive these interactions are up to 4.2 percentage points (pp) more likely to join fringe communities than similar, matched users who do not. This effect is influenced by 1) the characteristics of communities where the interaction happens (e.g., left vs. right-leaning communities) and 2) the language used in the interactions. Interactions using toxic language have a 5pp higher chance of attracting newcomers to fringe communities than non-toxic interactions. We find no effect when repeating this analysis by replacing fringe (r/Incel, r/GenderCritical, and r/The_Donald) with non-fringe communities (r/climatechange, r/NBA, r/leagueoflegends), suggesting this growth mechanism is specific to fringe communities. Overall, our findings suggest that curtailing fringe-interactions may reduce the growth of fringe communities on mainstream platforms.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 60 references that are in the map. First posted on arXiv 18 Oct 2023.

Builds on

18 of 60 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 377
  4. 2020The Pushshift Reddit Datasetcited by 289
  5. 2023Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modelingcited by 94
  6. 2020Quantifying social organization and political polarization in online platformscited by 30
  7. 2017Community Identity and User Engagement in a Multi-Community Landscapecited by 18
  8. 2020Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generationcited by 18
  9. 2020Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Redditcited by 16
  10. 2020Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incelscited by 15
  11. 2022Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banningcited by 14
  12. 2020Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inferencecited by 13
6 more
  1. 2022Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donaldcited by 13
  2. 2022Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussionscited by 8
  3. 2022Understanding Online Migration Decisions Following the Banning of Radical Communitiescited by 6
  4. 2021Why do people participate in small online communities?cited by 3
  5. 2021Soros, Child Sacrifices, and 5G: Understanding the Spread of Conspiracy Theories on Web Communitiescited by 3
  6. 2022Quantifying How Hateful Communities Radicalize Online Userscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.