Quantifying opinion homophily in online social networks: A bounded confidence perspective

TL;DR

This study investigates opinion-based homophily in online social networks, revealing that users tend to cluster around similar opinions, with tie strength and polarization intensifying this effect, supporting a bounded confidence model.

Contribution

It introduces a bounded confidence framework to analyze opinion homophily in social media, using sentiment and fact-checking data across Reddit and Twitter.

Findings

Interaction neighborhoods are more opinion-concentrated than chance

Tie strength and polarization amplify opinion homophily

Users show asymmetric tolerance, favoring mainstream positions

Abstract

The concept of homophily is pervasive in online social media. While many empirical studies have relied on external sociodemographic traits to investigate it, significantly less is known about homophily at the cognitive level, that is, at the level of shared opinions or values. For such "value homophily", in this paper we study interval-based patterns of opinion homophily from a bounded confidence perspective. We consider three heterogeneous datasets from Reddit and Twitter covering polarizing issues, with user opinions quantified via sentiment analysis and fact-checking, and analyze the interaction networks formed by weaker (reply-based) and stronger (follow-based) social ties. Our findings show that users' interaction neighborhoods are significantly more concentrated in opinion space than expected by chance, with tie strength and issue polarization further amplifying this effect. Moreover, users often exhibit asymmetric tolerance ranges, with asymmetry typically directed toward locally mainstream positions rather than more radical or opposing ones. These findings support a bounded confidence interpretation of online value homophily.