Topic-wise Exploration of the Telegram Group-verse

Although Telegram is currently one of the most popular instant messaging apps in the world, previous studies have mainly focused on analysing discussions on specific angles and topics. In this paper, we present a broad analysis of publicly accessible groups that cover a wide range of discussions, including Education, Erotic, Politics, and Cryptocurrencies. How do people interact with different topic groups? Is there any common or peculiar behaviour? We engineer and offer an open-source tool to automate the collection of messages from Telegram groups, a non-straightforward problem. We use it to collect more than 51 million messages from 669 groups. Here, we present a first-of-its-kind, per-topic analysis, contrasting the users' activity patterns from different angles - the language, the presence of bots, the type and volume of shared media content, links to external platforms, etc. Our results confirm some anecdotal evidence, e.g., indications of spamming behaviour, and unveil some unexpected findings, e.g., the different sharing patterns of video and message length in groups of different topics. Our research provides a horizontal analysis of the public group in Telegram across various general topics, establishing a foundation for future studies that can delve deeper into user interactions and content dynamics within this unique messaging environment.

Authors

Institutions

Publication Details

Published
2025-05-08
DOI
https://doi.org/10.1145/3701716.3717506
Citations
4
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
5.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Topic-wise Exploration of the Telegram Group-verse

Marco Mellia, Luca Vassio, Nikhil Jha, Jussara M. Almeida et al.
4 citations
Complex Network Analysis Techniques
5.29
article

Topic-wise Exploration of the Telegram Group-verse

Marco Mellia, Luca Vassio, Nikhil Jha, Jussara M. Almeida, Giordano Paoletti, Alessandro Perlo
article en
4 citations

Abstract

Although Telegram is currently one of the most popular instant messaging apps in the world, previous studies have mainly focused on analysing discussions on specific angles and topics. In this paper, we present a broad analysis of publicly accessible groups that cover a wide range of discussions, including Education, Erotic, Politics, and Cryptocurrencies. How do people interact with different topic groups? Is there any common or peculiar behaviour? We engineer and offer an open-source tool to automate the collection of messages from Telegram groups, a non-straightforward problem. We use it to collect more than 51 million messages from 669 groups. Here, we present a first-of-its-kind, per-topic analysis, contrasting the users' activity patterns from different angles - the language, the presence of bots, the type and volume of shared media content, links to external platforms, etc. Our results confirm some anecdotal evidence, e.g., indications of spamming behaviour, and unveil some unexpected findings, e.g., the different sharing patterns of video and message length in groups of different topics. Our research provides a horizontal analysis of the public group in Telegram across various general topics, establishing a foundation for future studies that can delve deeper into user interactions and content dynamics within this unique messaging environment.

Universidade Federal de Minas Gerais (BR), Politecnico di Torino (IT)
European Commission, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais
Openalex Percentile: Top 4%
Complex Network Analysis Techniques
5.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.