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

2025-11-21

AI Summary

This study investigates how community norms shape user toxicity in social media. Method: Leveraging over 500 million multilingual messages from Telegram (2015–2024), we develop a “Conformity Index” framework to quantify users’ tendencies toward conformity, counter-conformity, and independence across groups. Integrating cross-lingual toxicity detection, large-scale text analysis, and social-behavior modeling, we conduct the first empirical examination of norm-driven toxicity adaptation. Contribution/Results: We find that users significantly calibrate their toxic expression to match group-level normativity—particularly as engagement increases—and this effect is robust across English, Russian, Arabic, and other language communities. Moving beyond individual-trait paradigms, our work establishes community norms as a key structural driver of online toxicity, offering empirically grounded, community-level metrics for platform governance and intervention design.

Technology Category

• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

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.

Problem

• Investigating how local community norms influence toxic expression online
• Analyzing user adaptation to social norms through behavioral conformity modeling
• Understanding how exposure to chat environments shapes toxic behavior conformity

Innovation

• Modeling user adaptation with conformity index
• Analyzing 500 million Telegram messages across languages
• Measuring conformity to local community toxicity norms