From Toxicity to Conformity: Adaptive user behavior to social norms in Telegram communities

TL;DR

This study investigates how local community norms influence toxic behavior on Telegram, revealing that most users tend to conform to the toxicity levels of their chat environments, with social influence reinforcing these patterns.

Contribution

It introduces a novel conformity index to model user adaptation to community norms across large-scale multilingual datasets, advancing understanding of toxicity dynamics online.

Findings

Most users conform to local toxicity levels

Community norms significantly influence user behavior

Exposure to norms increases conformity tendencies

Abstract

Toxic and antisocial user behavior on social media platforms has received considerable scholarly attention due to its detrimental effects on society. This study takes a holistic perspective on the phenomenon of online toxicity by investigating the impact of local community norms on toxic expression. By using six large-scale datasets, comprising over 500 million Telegram messages collected between 2015 and 2024, we analyze toxic user behavior across multiple chats and languages. We introduce a methodological framework that models user adaptation through a conformity index, capturing conformist, anti-conformist, and independent behavioral tendencies. Our findings show that most users tend to conform to local normative environments, adjusting their toxicity to match the toxicity levels of the chats in which they participate. These patterns are consistent across datasets and languages, suggesting that community norms and social influence play a decisive role in shaping user behavior online. Furthermore, we demonstrate that exposure to these norms, in terms of increased user participation in chats, is associated with a stronger tendency toward conformity with the surrounding social contexts. Collectively, these findings contribute to a deeper understanding of toxic online behavior and highlight the importance of contextualized approaches to content moderation.