# Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study

arXiv: https://arxiv.org/abs/2502.02943 · HTML: https://mlsanity.com/paper/2502.02943/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 14 of 14)

- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Echo Chambers: Emotional Contagion and Group Polarization on Facebook](https://mlsanity.com/paper/1607.01032/) (2016)
- [No Echo in the Chambers of Political Interactions on Reddit](https://mlsanity.com/paper/2102.05477/) (2021)
- [Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior](https://mlsanity.com/paper/1910.11703/) (2019)
- [Roots of Trumpism: Homophily and Social Feedback in Donald Trump Support on Reddit](https://mlsanity.com/paper/2005.01790/) (2020)
- [Practical Guidelines for Ideology Detection Pipelines and Psychosocial Applications](https://mlsanity.com/paper/2208.04097/) (2022)
- [Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Reddit](https://mlsanity.com/paper/2211.14388/) (2022)
- [Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit](https://mlsanity.com/paper/2302.07598/) (2023)
- [Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine](https://mlsanity.com/paper/2307.12788/) (2023)
- [Linking the Dynamics of User Stance to the Structure of Online Discussions](https://mlsanity.com/paper/2101.09852/) (2021)

## Built on by (most cited first, 6 of 6)

- [Learning to Make Friends: Coaching LLM Agents toward Emergent Social Ties](https://mlsanity.com/paper/2510.19299/) (2025)
- [Mario at EXIST 2025: A Simple Gateway to Effective Multilingual Sexism Detection](https://mlsanity.com/paper/2507.10996/) (2025)
- [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025)
- [DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Media](https://mlsanity.com/paper/2602.01567/) (2026)
- [Beyond Content: Behavioral Policies Reveal Actors in Information Operations](https://mlsanity.com/paper/2602.02838/) (2026)
- [Misinformation Without Triggers: From Factual Answers to Downstream Decisions](https://mlsanity.com/paper/2610.02886/) (2026)

## Abstract (arXiv)

Online communities play a critical role in shaping societal discourse and influencing collective behavior in the real world. The tendency for people to connect with others who share similar characteristics and views, known as homophily, plays a key role in the formation of echo chambers which further amplify polarization and division. Existing works examining homophily in online communities traditionally infer it using content- or adjacency-based approaches, such as constructing explicit interaction networks or performing topic analysis. These methods fall short for platforms where interaction networks cannot be easily constructed and fail to capture the complex nature of user interactions across the platform. This work introduces a novel approach for quantifying user homophily. We first use an Inverse Reinforcement Learning (IRL) framework to infer users' policies, then use these policies as a measure of behavioral homophily. We apply our method to Reddit, conducting a case study across 5.9 million interactions over six years, demonstrating how this approach uncovers distinct behavioral patterns and user roles that vary across different communities. We further validate our behavioral homophily measure against traditional content-based homophily, offering a powerful method for analyzing social media dynamics and their broader societal implications. We find, among others, that users can behave very similarly (high behavioral homophily) when discussing entirely different topics like soccer vs e-sports (low topical homophily), and that there is an entire class of users on Reddit whose purpose seems to be to disagree with others.