The Pushshift Telegram Dataset
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Abstract (arXiv)
Messaging platforms, especially those with a mobile focus, have become increasingly ubiquitous in society. These mobile messaging platforms can have deceivingly large user bases, and in addition to being a way for people to stay in touch, are often used to organize social movements, as well as a place for extremists and other ne'er-do-well to congregate. In this paper, we present a dataset from one such mobile messaging platform: Telegram. Our dataset is made up of over 27.8K channels and 317M messages from 2.2M unique users. To the best of our knowledge, our dataset comprises the largest and most complete of its kind. In addition to the raw data, we also provide the source code used to collect it, allowing researchers to run their own data collection instance. We believe the Pushshift Telegram dataset can help researchers from a variety of disciplines interested in studying online social movements, protests, political extremism, and disinformation.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 2; 2025: 4; 2026: 2.
12 papers.
- 2024MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Textscited by 14
- 2025TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulationcited by 13
- 2021Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Communitycited by 6
- 2024More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegramcited by 6
- 2021Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movementscited by 5
- 2023Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegramcited by 5
- 2025Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speechcited by 2
- 2025AMAQA: A Metadata-based QA Dataset for RAG Systemscited by 2
- 2025MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messagescited by 1
- 2023URM4DMU: an user represention model for darknet markets userscited by 0
- 2026PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegramcited by 0
- 2026Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Onlinecited by 0
- 2026Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Online1 Sep 2026
- 2026PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegram10 May 2026
- 2025TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation29 Oct 2025
- 2025MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages27 Jun 2025
- 2025AMAQA: A Metadata-based QA Dataset for RAG Systems19 May 2025
- 2025Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speech28 Apr 2025
- 2024More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram11 Oct 2024
- 2024MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts18 Jun 2024
- 2023URM4DMU: an user represention model for darknet markets users19 Mar 2023
- 2023Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram25 Jan 2023
- 2021Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements26 Nov 2021
- 2021Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community15 Sep 2021