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arxiv: 2605.15956 · v1 · pith:CU2Z24FFnew · submitted 2026-05-15 · ⚛️ physics.soc-ph

TeraGram: A Structured Longitudinal Dataset of the Telegram Messenger

Anastasia Golovin , Sebastian B. Mohr , Arne I. Gottwald , Ulrik Hvid , Srushhti Trivedi , Joao Pinheiro Neto , Andreas C. Schneider , Viola Priesemann This is my paper

Pith reviewed 2026-05-19 19:14 UTC · model grok-4.3

classification ⚛️ physics.soc-ph
keywords Telegramlongitudinal datasetpublic messagesalgorithm-free platformengagement patternscommunity formationnetwork evolutionsocial media data
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The pith

A dataset of 5.9 billion Telegram messages collected from 2015 to 2025 supplies raw data for examining social networks free of algorithmic curation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper releases TeraGram, a longitudinal collection of public Telegram content that includes more than 5.9 billion messages drawn from over 712 thousand channels and groups. The data carries metadata on forwards, reactions, and polls and spans Russian, Farsi, and several Western languages. The authors highlight that the material comes from a platform whose public channels operate without the opaque recommendation systems common on other social media. A sympathetic reader would value the resource because it permits direct comparison of engagement, community growth, and information flow under uniform platform rules but across different languages and user bases. Such a resource could clarify how groups and networks develop when content selection is driven only by user choices rather than hidden algorithms.

Core claim

The authors present TeraGram as a structured longitudinal dataset of public Telegram messages that comprises over 5.9 billion items from 2015 to 2025 across 712 thousand channels and groups. The collection includes metadata on forwards, reactions, and polls and covers Russian, Farsi, and Western languages. The dataset is offered as an example of an algorithm-free platform that supports comparative studies of engagement patterns, network evolution, and community formation under identical platform affordances.

What carries the argument

The TeraGram dataset itself, which aggregates public messages and associated metadata to support analysis of engagement and network dynamics in the absence of content-curation algorithms.

If this is right

  • Comparative studies of the same platform affordances become possible across languages and regions where Telegram serves different user bases.
  • Longitudinal tracking of network growth and community formation can be performed without the confounding influence of recommendation algorithms.
  • Analyses of engagement can use concrete signals such as reaction counts, forward chains, and poll participation.
  • Researchers gain a shared data resource for testing claims about information spread in environments that lack opaque curation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The dataset could be used to test whether network structures observed on algorithm-driven platforms arise mainly from user choices or from platform interventions.
  • Cross-language subsets might reveal how mainstream adoption in one region differs from niche use in another under the same technical rules.
  • Future work could link message-level metadata to external events to measure response times in uncurated channels.

Load-bearing premise

That messages drawn only from public channels and filtered by language give a representative picture of how users actually behave on Telegram.

What would settle it

A direct comparison showing that engagement rates or topic distributions in private Telegram groups differ substantially from those recorded in the public portion of the dataset.

Figures

Figures reproduced from arXiv: 2605.15956 by Anastasia Golovin, Andreas C. Schneider, Arne I. Gottwald, Joao Pinheiro Neto, Sebastian B. Mohr, Srushhti Trivedi, Ulrik Hvid, Viola Priesemann.

Figure 1
Figure 1. Figure 1: A simplified entity-relation diagram of the dataset. See SI, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Number of messages posted per week in the dataset. The time series spans September 2015 to November 2025. Apparent variations during the collection period are influ￾enced by the crawling procedure: chats were downloaded only once, so those discovered later in the crawl contribute more recent messages than chats collected earlier. The dataset is shared under a two-tiered access model: rich metadata is openl… view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the message length shows sev￾eral distinct peaks caused by systematic patterns. Star: YouTube links; square: message redacted by Telegram for copyright or Terms of Service violation; diamond: an adver￾tisement message mass-posted in one Arabic channel; triangle: caption length limit. Finally, we performed n-gram analysis to detect artifacts in text such as unusual punctuation, spam, and nea… view at source ↗
Figure 5
Figure 5. Figure 5: Languages of fully downloaded chats. Chat language is classified based on the text of the first 100 messages. Language codes follow the ISO standard. Error bars give the 95% CI interval. mains like search engines and social media platforms that appear in the Lin et al. dataset but do not refer to news sources (SI, Tab. 7). Of the remaining URLs, we randomly sampled 1% to reduce computational costs, which r… view at source ↗
Figure 6
Figure 6. Figure 6: High prevalence of unreliable URLs in English￾speaking Telegram chats compared to a mainstream platform like Twitter. The bands represent the 95% CI in￾terval obtained by clustered bootstrapping on domains. Overall, we observe a high prevalence of URLs with a reli￾ability score below 0.6 in English-speaking Telegram chats ( [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Topics in Russian and English chats identified using BERTopic. While both English and Russian datasets contain topics like sports and current events, the English dataset includes a subset of far-right topics (e.g., “antisemitic narratives,” “climate change hoax”). In contrast, Russian topics predominantly reflect mainstream diverse interests, including books, fashion, art, and music. See Tables 8-11 in the… view at source ↗
Figure 8
Figure 8. Figure 8: Entity-relation diagram of the SQL database. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: A sketch of the crawling algorithm. The crawler discovers new chats through forwarded messages. The chats are then prioritized for download based on their out-degree, i.e., the number of forwarded messages from this chat into already downloaded chats. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Topics in Farsi and Arabic chats identified using BERTopic. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents TeraGram, a longitudinal dataset of over 5.9 billion public Telegram messages collected from 712 thousand channels and groups between 2015 and 2025. The data include metadata on forwards, reactions, and polls, and span multiple languages (Russian, Farsi, and Western languages). The central claims are that language-restricted subsets offer a versatile example of an algorithm-free platform and enable comparative studies of engagement, network evolution, and community formation under identical platform affordances.

Significance. If the collection and documentation are completed to address sampling and bias concerns, the dataset's scale and longitudinal span would constitute a useful resource for social-physics and computational-social-science research on platform dynamics without algorithmic curation. The explicit framing for cross-language comparisons under fixed affordances is a constructive contribution that could support falsifiable analyses of engagement patterns.

major comments (2)
  1. [§2] §2 (Data Collection): The manuscript supplies no description of the channel/group discovery or sampling procedure. It is therefore impossible to assess whether the 712k sources were obtained via Telegram search, popularity signals, or exhaustive crawling; any reliance on discoverability metrics would introduce selection bias that directly undermines the abstract's claim that language-restricted subsets yield a representative view of algorithm-free user behavior.
  2. [Abstract, §4] Abstract and §4 (Advantages): The stated advantage that the dataset provides 'a versatile example of an algorithm-free platform' is load-bearing for the paper's utility argument, yet no validation steps, bias audits, or comparison against Telegram's full public population are reported. Without these, the representativeness required for studies of engagement and community formation cannot be evaluated.
minor comments (2)
  1. [Table 1] Table 1 (dataset statistics): clarify whether the 5.9B message count includes only text or also media and poll items; the current caption leaves this ambiguous.
  2. [§3] §3 (Metadata): the forward and reaction fields are described at a high level; explicit schema definitions or example JSON records would improve reproducibility for downstream users.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and for identifying key areas where additional documentation and clarification would strengthen the manuscript. We address each major comment below, indicating the revisions made to improve transparency around sampling and to moderate the claims regarding representativeness.

read point-by-point responses

  1. Referee: [§2] §2 (Data Collection): The manuscript supplies no description of the channel/group discovery or sampling procedure. It is therefore impossible to assess whether the 712k sources were obtained via Telegram search, popularity signals, or exhaustive crawling; any reliance on discoverability metrics would introduce selection bias that directly undermines the abstract's claim that language-restricted subsets yield a representative view of algorithm-free user behavior.

    Authors: We agree that the absence of a sampling description in §2 prevents proper evaluation of selection bias. The original manuscript omitted this detail, focusing instead on the resulting data volume and structure. Collection proceeded via Telegram's public search functionality using language-specific keywords and seed channels drawn from publicly available directories, followed by iterative expansion through forward metadata. This approach necessarily favors discoverable sources and may under-represent private or low-visibility groups. In the revised manuscript we have inserted a new subsection in §2 that fully documents the discovery pipeline, the temporal windows of crawling, the keyword sets employed, and an explicit discussion of the resulting coverage limitations and potential biases. These additions directly enable readers to assess the dataset's suitability for claims about algorithm-free behavior. revision: yes

  2. Referee: [Abstract, §4] Abstract and §4 (Advantages): The stated advantage that the dataset provides 'a versatile example of an algorithm-free platform' is load-bearing for the paper's utility argument, yet no validation steps, bias audits, or comparison against Telegram's full public population are reported. Without these, the representativeness required for studies of engagement and community formation cannot be evaluated.

    Authors: We accept that the original phrasing overstated the dataset's representativeness without supporting evidence. Telegram public channels and groups operate without the centralized recommendation algorithms that dominate other platforms, making discovery more dependent on explicit user actions such as search and forwarding; however, we did not perform a systematic audit against Telegram's complete public population. In the revised abstract and §4 we have replaced the stronger claim with more precise language stating that the dataset supplies a large-scale, longitudinal record of public content collected under Telegram's native affordances, which lack opaque algorithmic curation. We have added a limitations paragraph that acknowledges the absence of exhaustive population benchmarks and outlines how downstream users can mitigate sampling biases when conducting comparative or engagement analyses. This revision preserves the dataset's utility for cross-language studies while avoiding unsubstantiated representativeness assertions. revision: yes

Circularity Check

0 steps flagged

No circularity: data-release paper with no derivations or predictions

full rationale

This is a dataset description paper whose central claim is the existence, scale, and described utility of the collected TeraGram dataset for studying engagement on an algorithm-free platform. The abstract and full text contain no equations, fitted parameters, predictions, or derivation chains. No load-bearing steps reduce by construction to self-definitions, fitted inputs, or self-citation chains. Any self-citations (if present) support factual collection details rather than a mathematical result that is forced by the citation itself. The paper is self-contained against external benchmarks as a factual data release; the skeptic concern about selection bias is a question of representativeness, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The utility of the dataset rests on assumptions about data representativeness and platform properties rather than on fitted parameters or new entities.

axioms (2)
  • domain assumption Public Telegram content can be collected at scale in a manner that yields representative samples for studying natural engagement patterns.
    Invoked when the abstract claims the dataset enables studies of engagement without algorithmic curation.
  • domain assumption Restricting the data by language produces a versatile example of an algorithm-free platform.
    Directly stated as the first advantage in the abstract.

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