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

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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

To research the illegal activities of underground apps on Telegram, we have created a dataset called TUApps. TUApps is a progressively growing dataset of underground apps, collected from September 2023 to February 2024, consisting of a total of 1,000 underground apps and 200 million messages distributed across 71,332 Telegram channels.
In the process of creating this dataset, we followed strict ethical standards to ensure the lawful use of the data and the protection of user privacy. The dataset includes the following files:
(1) dataset.zip: We have packaged the underground app samples. The naming of Android app files is based on the SHA256 hash of the file, and the naming of iOS app files is based on the SHA256 hash of the publishing webpage.
(2) code.zip: We have packaged the code used for crawling data from Telegram and for performing data analysis.
(3) message.zip: We have packaged the messages crawled from Telegram, the files are named after the names of the channels in Telegram.
Availability of code and messages
Upon acceptance of our research paper, the dataset containing user messages and the code used for data collection and analysis will only be made available upon request to researchers who agree to adhere to strict ethical principles and maintain the confidentiality of the data.

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This Dataset is described in Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020), a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.

Please, refer to the paper below for more details.

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).

This dataset is composed of:

~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API;

~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL);

~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight).

With such information, one can easily retrieve the content of channels and messages through Twitter, Telegram, and Discord public APIs.

Please, refer to the README file for more details about the fields.

Online messaging app Telegram has increased in popularity in recent years surpassing Twitter and Snapchat by the number of active monthly users in late 2020. The messenger has also been crucial to protest movements in several countries in 2019-2020, including Belarus, Russia and Hong Kong. Yet, to date only few studies examined online activities on Telegram and none have analyzed the platform with regard to the protest mobilization. In the present study, we address the existing gap by examining Telegram-based activities related to the 2019 protests in Hong Kong. With this paper we aim to provide an example of methodological tools that can be used to study protest mobilization and coordination on Telegram. We also contribute to the research on computational text analysis in Cantonese - one of the low-resource Asian languages, - as well as to the scholarship on Hong Kong protests and research on social media-based protest mobilization in general. For that, we rely on the data collected through Telegram’s API and a combination of network analysis and computational text analysis. We find that the Telegram-based network was cohesive ensuring efficient spread of protest-related information. Content spread through Telegram predominantly concerned discussions of future actions and protest-related on-site information (i.e., police presence in certain areas). We find that the Telegram network was dominated by different actors each month of the observation suggesting the absence of one single leader. Further, traditional protest leaders - those prominent during the 2014 Umbrella Movement, - such as media and civic organisations were less prominent in the network than local communities. Finally, we observe a cooldown in the level of Telegram activity after the enactment of the harsh National Security Law in July 2020. Further investigation is necessary to assess the persistence of this effect in a long-term perspective.

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