Join the Chat: How Curiosity Sparks Participation in Telegram Groups

2025-03-17

AI Summary

This study investigates the core curiosity-inducing mechanisms driving proactive message posting in public Telegram groups. Method: Leveraging ~6 million temporal messages from 29,196 users across 409 groups, we propose the first multidimensional curiosity-stimulus profiling framework that jointly models message sequences and participation dynamics—quantifying five stimulus dimensions: social influence, novelty, complexity, uncertainty, and conflict. We integrate unsupervised clustering, XAI-based label generation, graph neural networks, and social network centrality analysis. Contribution/Results: We identify temporal stability and gradual evolution in users’ curiosity preferences; further, we uncover structural associations between stimulus types and network positions: socially influenced users predominantly occupy peripheral roles, whereas users with no dominant preference tend to reside at structural centers and initiate conversations. These findings provide novel empirical evidence and an interpretable, mechanism-aware modeling paradigm for designing interactive features and understanding information diffusion in social platforms.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Causality

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Social mining and social search on the Web
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

This study delves into the mechanisms that spark user curiosity driving active engagement within public Telegram groups. By analyzing approximately 6 million messages from 29,196 users across 409 groups, we identify and quantify the key factors that stimulate users to actively participate (i.e., send messages) in group discussions. These factors include social influence, novelty, complexity, uncertainty, and conflict, all measured through metrics derived from message sequences and user participation over time. After clustering the messages, we apply explainability techniques to assign meaningful labels to the clusters. This approach uncovers macro categories representing distinct curiosity stimulation profiles, each characterized by a unique combination of various stimuli. Social influence from peers and influencers drives engagement for some users, while for others, rare media types or a diverse range of senders and media sparks curiosity. Analyzing patterns, we found that user curiosity stimuli are mostly stable, but, as the time between the initial message increases, curiosity occasionally shifts. A graph-based analysis of influence networks reveals that users motivated by direct social influence tend to occupy more peripheral positions, while those who are not stimulated by any specific factors are often more central, potentially acting as initiators and conversation catalysts. These findings contribute to understanding information dissemination and spread processes on social media networks, potentially contributing to more effective communication strategies.

Problem

• Identify factors driving user engagement in Telegram groups.
• Analyze social influence, novelty, and media diversity effects.
• Understand curiosity shifts and user roles in influence networks.

Innovation

• Analyzed 6M messages for user engagement factors.
• Applied explainability techniques to cluster messages.
• Used graph-based analysis to map influence networks.