Join the Chat: How Curiosity Sparks Participation in Telegram Groups

TL;DR

The paper investigates what triggers curiosity-driven participation in public Telegram groups by introducing a multifactored curiosity framework that includes novelty, uncertainty, complexity, conflict, and social influence. It operationalizes these factors with nine information-theoretic metrics computed in a fixed interaction window of $\Delta T=30$ minutes, then clusters messages to identify six distinct curiosity profiles and analyzes user-level engagement and influence through OLS modeling and influence-by networks. Key findings show that profiles driven by uncertainty or indirect social influence tend to correlate with higher engagement and centrality, while novelty and direct social influence often relate to lower activity and more peripheral roles; independent users frequently serve as conversation initiators. The study demonstrates topic-specific variability in curiosity stimuli and provides a framework to understand information dissemination dynamics in large online groups, offering insights for designing engagement strategies in social media ecosystems.

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.

Figures (14)

• Figure 1: Overview of analyzed Telegram dataset.
• Figure 2: Correlation among curiosity metrics for all messages.
• Figure 3: Distributions of Euclidean distances in the curiosity stimulus space between the messages sent by a user and (i) the barycenter of all messages by the same user (blue), (ii) the centroid of each message's profile (orange) and (iii) the barycenter of messages sent by other users in the last 15 minutes (green).
• Figure 4: Distribution of the probability $p_{trans}$ that the next message from the same user in a group belongs to a different curiosity stimulus cluster, conditioned on whether the time interval $\delta_t$ between them exceeds a certain threshold.
• Figure 5: Average fraction of messages sent in each stimulus curiosity cluster from users belonging to a group of the topic (blue solid line). The dotted grey line is the average distribution over the entire population. Dashed green/red lines highlight that the distribution in the topic population is statistically higher/lower than the general one.