Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study
Lanqin Yuan, Philipp J. Schneider, Marian-Andrei Rizoiu

TL;DR
This paper introduces a novel method using inverse reinforcement learning to measure behavioral homophily on Reddit, revealing complex user interaction patterns and their societal implications beyond traditional content-based analysis.
Contribution
It proposes a new IRL-based approach to quantify behavioral homophily in social media, applicable when interaction networks are hard to construct.
Findings
High behavioral homophily occurs across different topics like soccer and e-sports.
A significant user group on Reddit tends to disagree with others.
Behavioral patterns vary across communities and user roles.
Abstract
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…
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