# Hatemongers ride on echo chambers to escalate hate speech diffusion

arXiv: https://arxiv.org/abs/2302.02479 · HTML: https://mlsanity.com/paper/2302.02479/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [Algorithmic Amplification of Politics on Twitter](https://mlsanity.com/paper/2110.11010/) (2021)
- [A Unified Deep Learning Architecture for Abuse Detection](https://mlsanity.com/paper/1802.00385/) (2018)
- [Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter](https://mlsanity.com/paper/1803.03662/) (2018)
- [Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship](https://mlsanity.com/paper/1801.01665/) (2018)
- [What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber?](https://mlsanity.com/paper/1802.05287/) (2018)
- [Me, My Echo Chamber, and I: Introspection on Social Media Polarization](https://mlsanity.com/paper/1803.01731/) (2018)
- [Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter](https://mlsanity.com/paper/2010.04377/) (2020)
- [Lifelong Learning of Hate Speech Classification on Social Media](https://mlsanity.com/paper/2106.02821/) (2021)
- [Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms](https://mlsanity.com/paper/2004.14879/) (2020)
- [Extremist ideology as a complex contagion: the spread of far-right radicalization in the United States between 2005-2017](https://mlsanity.com/paper/2004.04265/) (2020)
- [Making a Radical Misogynist: How online social engagement with the Manosphere influences traits of radicalization](https://mlsanity.com/paper/2202.08805/) (2022)

## Built on by (most cited first, 4 of 4)

- [Modeling Empathic Similarity in Personal Narratives](https://mlsanity.com/paper/2305.14246/) (2023)
- [SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech Detection in Memes](https://mlsanity.com/paper/2412.20541/) (2024)
- [Analyzing User Characteristics of Hate Speech Spreaders on Social Media](https://mlsanity.com/paper/2310.15772/) (2023)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)

## Abstract (arXiv)

Recent years have witnessed a swelling rise of hateful and abusive content over online social networks. While detection and moderation of hate speech have been the early go-to countermeasures, the solution requires a deeper exploration of the dynamics of hate generation and propagation. We analyze more than 32 million posts from over 6.8 million users across three popular online social networks to investigate the interrelations between hateful behavior, information dissemination, and polarised organization mediated by echo chambers. We find that hatemongers play a more crucial role in governing the spread of information compared to singled-out hateful content. This observation holds for both the growth of information cascades as well as the conglomeration of hateful actors. Dissection of the core-wise distribution of these networks points towards the fact that hateful users acquire a more well-connected position in the social network and often flock together to build up information cascades. We observe that this cohesion is far from mere organized behavior; instead, in these networks, hatemongers dominate the echo chambers -- groups of users actively align themselves to specific ideological positions. The observed dominance of hateful users to inflate information cascades is primarily via user interactions amplified within these echo chambers. We conclude our study with a cautionary note that popularity-based recommendation of content is susceptible to be exploited by hatemongers given their potential to escalate content popularity via echo-chambered interactions.