Toxic behavior silences online political conversations

TL;DR

This study investigates how toxic online behavior influences political conversations by causing self-censorship, revealing that toxicity can suppress minority opinions and foster environments of antisocial behavior in digital political discourse.

Contribution

It introduces a novel analysis of self-censorship driven by toxicity in online political discussions using hidden Markov models, highlighting the impact of toxic environments on ideological polarization.

Findings

Toxicity correlates with increased self-censorship among users.

Toxic environments are characterized by reduced activity and more toxic posts.

Self-censorship driven by toxicity influences online political polarization.

Abstract

Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation in online spaces by exploring the hypothesis that individuals may refrain from expressing minority opinions publicly due to being exposed to toxic behavior. Analyzing conversations under YouTube videos from six prominent US news outlets around the 2020 US presidential elections, we observe patterns of self-censorship signaling the influence of peer toxicity on users' behavior. Using hidden Markov models, we identify a latent state consistent with toxicity-driven silence. Such state is characterized by reduced user activity and a higher likelihood of posting toxic content, indicating an environment where extreme and antisocial behaviors thrive. Our findings offer insights into the intricacies of online political deliberation and emphasize the importance of considering self-censorship dynamics to properly characterize ideological polarization in digital spheres.