Hamster Kombat Announcement (@hamster_kombat) was the most-subscribed channel on the messaging platform Telegram, with around 43 million followers as of February 25, 2025. It is a Telegram-based game connected to the blockchain Hamster Network. Blum, a cryptocurrency trading app, ranked second with over 30 million subscribers. Selling and buying Telegram usernames Starting from end-October 2022, Telegram allowed its users to sell and buy usernames using the auction platform Fragment that used the cryptocurrency Toncoin (TON) for payments. As of February 2025, the most expensive username sold was @news. Its price stood at 994 thousand TON, or around 2.4 million U.S. dollars based on an exchange rate of 1 TON = 2.4 U.S. dollars as of December 2022). In total, as of November 30, 2022, Telegram usernames worth 50 million U.S. dollars were sold worldwide, as stated by Telegram’s owner Pavel Durov. Most popular Telegram channels in Russia In Russia, where Telegram originated, the most-subscribed Telegram channel is Meta Silense TON. As of February 2025, it had approximately 5.5 million followers. Besides the audience, performance of Telegram channels can be measured by views, reactions, and shares. In terms of average views per post, the leading channel in January 2025 belonged to Dmitry Medvedev, the former president of Russia.

The largest share of Telegram channel administrators as of 2021 was between 25 and 34 years of age, exceeding ** percent. Channel administrators who were younger than 24 years in this messaging app accounted for ** percent.

Топор+ (Topor+) had the largest average weekly share among Telegram channels in Russia, at *** percent of the population in the first quarter of 2025. The channel of RIA Novosti, a news outlet, ranked third.

This data set consist of public Telegram channels, concentrated on German-language discussions of QAnon.

The date range of the data is from the creation of the channel to 01 July 2021.

To collect the data, I first downloaded the chat history of 3 channels (listed under "Primary"), counted the number of forwarded messages from other channels/accounts, and selected the top 5 most-forwarded-from channels/accounts from my Primary level, and used those most-forwarded-from channels/accounts as my Secondary level.

I then repeated the process for the Secondary level, downloaded the chat histories and determining for the Secondary level the most-forwarded-from channels/accounts - the top 5 for each channel/account in the Secondary level became the Tertiary level.

I repeated this for the members of the Tertiary level, downloading their chat histories and determining what channels/groups were forwarded into the Tertiary level, but stopped the process there. For the visualization, I used the unique channels/accounts as nodes and the forwarding of a message as an edge connecting nodes.

Also included in this data set are the full text histories of the channels I collected data from, in the "Corpus" folder. The text of the messages were extracted from the JSON files of the chat history, leaving only the content of the messages.

My own analysis of this dataset has been basic, but I hope other researchers find this data useful.WF Thomaswfthomas@protonmail.comwww.wfthomas.com2021USE WITH ATTRIBUTION ONLY

Anonymized message classification data from public Telegram channels pertaining to the paper "A Computational Analysis of Telegram's Narrative Affordances".

Overview Anonymized message classification data and actantial analyses of public Telegram channels pertaining to the paper "A Computational Analysis of Telegram's Narrative Affordances". Message classification data All files are included in the zipped folder 'narrative_affordances_data.zip' Each file contains the message classification data for a single Telegram channel. Numbered files are included for each of the six datasets (1 combined, 5 thematic) discussed in the paper. Actantial analysis Frequency lists of retrieved actants are included in the zipped folder 'overview_of_actants.zip'

This dataset contains data from the most popular Russian telegram channels with political agendas from 2017 to 2023. Data includes the name of the channel, its type, the message’s body, and references. The dataset does not include private information such as user's comments or personal messages. All posts were published either anonymously or via official accounts. A sample of the raw data was preprocessed with STM and classified using 75 topics. In total, there are 1.157.959 messages in the raw data and 586932 in the sample.

This data collection comprises a file with the raw data (all_texts_with_add_info.xlsx (in two parts)), a file with the pre-processed data (sample_stm.xlsx), the list of topics (topics.xlsx), the R code which has been used for pre-processing (stm_supplementary.R) and the documentation of data collection (Documentation of Data Collection.pdf).

This dataset contains the raw data used in the article “Using AI to detect misinformation and emotions on Telegram: a comparison with the media”, accepted for publication in index.comunicación. The data includes:
• Telegram dataset (tg_messages.csv): 54,456 posts extracted from 33 public Telegram channels between 23 July and 16 November 2023, related to the political debate around the Amnesty Law in Spain. Each entry includes message metadata such as channel, date, views, and content.
• News headlines dataset (Titulares.csv): 46,022 news headlines mentioning “amnesty”, extracted from 377 Spanish national media outlets indexed in MediaCloud, during the same period.
• Analysis scripts: Available upon request or pending publication in the article’s supplementary materials.

The data was used for topic modelling, sentiment and emotion detection with NLP techniques based on Python libraries like BERTopic and pysentimiento. All data is anonymized and publicly accessible or derived from open sources.

News channels were the most popular channel type on Telegram as of 2023, followed by 85 percent of app users. Channels with a focus on entertainment and education were read by 62 percent and 58 percent of users, respectively.

Telegram graph data of COVID-19 related channels Dataset of 128.148 Telegram channels/groups connected by 320.194.154 forwarded messages. Only vertices and edges are present with a limited amount of metadata. No message content is included. We include one files for the graph "graph.gt.gz" which includes the weighted directed graph. The weights can be found as edge properties. The file are in the gt format.

Through Telegram API, the authors collected this database over four months ago. These data are Telegram's comments of over eight professional Telegram channels about cryptocurrencies from December 2023 to March 2024. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets or Telegram's comments on one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity considerably. This database has a main table with eight columns. The columns of the main table are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Furthermore, we have added Python code to extract Telegram's comments. We used the RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer-based HDRB model(https://ieeexplore.ieee.org/document/10292644) for sentiment analysis.

TeleScope is an extensive dataset suite that comprises metadata for about 500K Telegram channels and downloaded message metadata from all 71K public channels within this 500k channels accounting for about 120M crawled messages. In addition to metadata, TeleScope suite provides enrichments like language detection and active periods for each channel and telegram entity extracted from messages. It also comprises channel connections and user interaction data built using Telegram’s message-forwarding feature to study multiple use cases including information spread and message-forwarding patterns. The dataset is designed for diverse applications, independent of specific research objectives, and sufficiently versatile to facilitate the replication of social media studies comparable to those conducted on platforms like X (former Twitter).

Further information on the content of the files can be found in the file TeleScope_readme_v1-0-0.txt (see 'Technical Report').

keywords: Computational Social Science; Information Science, Web and Social Media; text analysis; text processing; text communication; social media; Online discourse; Information Dissemination; Information Analysis

The highest share of Telegram channel administrators placed ads on their channels several times per week in 2023. Approximately *********** of channel administrators would place ads *** or *** times per month.

ThreatGram 101 - Extreme Telegram Replies Data with Threat Levels

  Description

Data 1: Raw and Unlabeled; 2 million unlabeled replies from 17 Telegram channels. Data 2: Raw and Labeled; 15,076 replies from 17 Telegram channels categorized as no threat, judicial threat, and non-judicial threat.

  Citation

If you use this dataset, please cite: @INPROCEEDINGS{10459792,
author={Ravi, Kamalakkannan and Vela, Adan Ernesto and Jenaway, Elizabeth and Windisch, Steven}… See the full description on the dataset page: https://huggingface.co/datasets/singlelabelmulticlass/ThreatGram101.

As of February 2025, the most-subscribed Telegram channel from Russia was Meta Silense TON, a cryptocurrency channel, with around *** million followers. The fifth-most subscribed channel was Topor Live ("Топор Live"), a private news channel.

This dataset contains about 8000 messages for two popular persian channel in telegram (gizmiz and official persian tweeter channel). for each message the message text and 5 top reactions for the message stored. this dataset can be used to train models to predict user reactions to messages in persian telegram channels. also this dataset can be used to explore about users opinion in telegram and know their interests.

"TikTok Updates" (@tiktokupdatez) was the leading Telegram channel about software and apps in Russia as of August 2025, with nearly *** million subscribers. The second-most popular channel was "TikTokModCloud" (@TikTokModCloud).

This dataset contains venting content scraped from the Ethiopian-based Telegram channel Vent Here. It has been pre-processed to remove non-English entries, emojis, and unwanted prefixes, with sentiment and emotion labels added for each entry.

Key Features

• Source: Sourced from the public Telegram channel Vent Here, where users share emotional experiences.
• Pre-processing:
  • Removed emojis
  • Cleaned text prefixes
  • Filtered out non-English text

Analysis

• Sentiment: Classified using a Hugging Face sentiment pipeline (positive, negative, neutral) with a sentiment score ranging from -1 (negative) to 1 (positive).
• Emotion: Analyzed using a RoBERTa-based model for emotion classification (e.g., happiness, sadness, anger).

Columns

• date: Timestamp of the venting content.
• text: Cleaned vent text.
• emotion_label: Predicted emotion.
• sentiment_label: Sentiment label (positive, negative, neutral).
• sentiment_score: Sentiment score, ranging from -1 to 1.

This dataset provides valuable insights into emotional expression and sentiment in online communities.

Approximately 41 percent of channel administrators on Telegram profiting from placing advertisements on the platform, earned up to five thousand Russian rubles per month, as of 2022. Over 15 percent of administrators received between six and 15 thousand Russian rubles from ads on their channels per month.