TeraGram: A Structured Longitudinal Dataset of the Telegram Messenger
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917500159000576 |
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| author | Golovin, Anastasia Mohr, Sebastian B. Gottwald, Arne I. Hvid, Ulrik Trivedi, Srushhti Neto, Joao Pinheiro Schneider, Andreas C. Priesemann, Viola |
| author_facet | Golovin, Anastasia Mohr, Sebastian B. Gottwald, Arne I. Hvid, Ulrik Trivedi, Srushhti Neto, Joao Pinheiro Schneider, Andreas C. Priesemann, Viola |
| contents | Here we present a massive longitudinal dataset of public Telegram content, comprising over 5.9 billion messages dating from 2015 to 2025, collected from 712 thousand channels and groups, enriched with metadata on forwards, reactions, and polls. The dataset spans multiple languages including Russian and Farsi, representing countries where Telegram shows mainstream adoption, as well as Western languages where Telegram is used in specific sub-communities. The dataset has several advantages. First, when restricted by language, it provides a versatile example of an algorithm-free platform, contrary to many other social media platforms that are strongly influenced by opaque content-curation algorithms. Second, it enables comparative studies across different languages, communities, and user bases under identical platform affordances. The dataset thus offers a foundation for studying engagement patterns, network evolution, and community formation in the absence of algorithmic curation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15956 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TeraGram: A Structured Longitudinal Dataset of the Telegram Messenger Golovin, Anastasia Mohr, Sebastian B. Gottwald, Arne I. Hvid, Ulrik Trivedi, Srushhti Neto, Joao Pinheiro Schneider, Andreas C. Priesemann, Viola Physics and Society Here we present a massive longitudinal dataset of public Telegram content, comprising over 5.9 billion messages dating from 2015 to 2025, collected from 712 thousand channels and groups, enriched with metadata on forwards, reactions, and polls. The dataset spans multiple languages including Russian and Farsi, representing countries where Telegram shows mainstream adoption, as well as Western languages where Telegram is used in specific sub-communities. The dataset has several advantages. First, when restricted by language, it provides a versatile example of an algorithm-free platform, contrary to many other social media platforms that are strongly influenced by opaque content-curation algorithms. Second, it enables comparative studies across different languages, communities, and user bases under identical platform affordances. The dataset thus offers a foundation for studying engagement patterns, network evolution, and community formation in the absence of algorithmic curation. |
| title | TeraGram: A Structured Longitudinal Dataset of the Telegram Messenger |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2605.15956 |