The largest share of Telegram users as of 2023 was between 25 and 34 years of age, at over ** percent. Users of the messaging app aged younger than 24 years accounted for over ** percent of the user base.

Introduction

Discord vs Telegram Statistics: Discord and Telegram are two of the most popular communication platforms today, each serving different audiences with unique features, strengths, and weaknesses. Discord is an instant messaging and VoIP social platform that allows communication through voice calls, video calls, text messaging, and media sharing. In contrast, Telegram, also known as Telegram Messenger, is a cloud-based, cross-platform messaging service that combines social media and instant messaging (IM).

This article will guide you effectively, as it provides a comprehensive comparison between the two platforms, including features, pricing, user experience, and performance, to help determine which platform best suits community-building tools or fast, secure messaging in 2025.

As of January 2025, monthly active users of Telegram accounted for over ** percent of the global population, marking an increase from the previous year. In total, *** million people used Telegram on average per month.

As of March 2025, cloud-based mobile messaging app Telegram reported over *********** monthly active users worldwide. Telegram is a chat app launched in 2013 by the brothers Nikolai and Pavel Durov. The pair had previously founded the Russian social network VK, which they left when it was taken over by the Mail.ru Group. Telegram In 2023, the majority of Telegram’s global audience, was comprised of users aged between 25 and 34 years. Among the reasons to use Telegram, ** percent of the platform’s users reported to learn the majority of the news from Telegram. After the announcement that WhatsApp was going to release a new privacy policy update was met with criticism in 2021, Telegram's audience and popularity spiked, bringing global downloads to *** million. Telegram security criticism Despite claiming to be more secure than other mainstream messengers such as Line or WhatsApp, Telegram has been frequently criticized by industry experts such as German consumer organization Stiftung Warentest or the Electronic Frontier Foundation (EFF). One of the key points of criticism is Telegram’s failure to provide automatic end-to-end encryption (WhatsApp and LINE messenger have end-to-end encryption set up by default).

The largest share of Telegram users as of 2023 were earning between 61 and 150 thousand Russian rubles per month, at about 17.3 percent. Users of the messaging app with income level ranging between 201 and 300 thousand Russian rubles monthly accounted for 6.4 percent of the user base.

The largest share of Telegram users as of 2023 were employed in the IT and Internet sector, at about 21 percent. Users of the messaging app with occupation in the production industry accounted for over seven percent of the user base.

The majority of Telegram users needed the application for personal correspondence as of 2023, at approximately 86 percent. More than sixty percent of the user base of the instant messaging app used it for communication in group chats.

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

India was the leading market of Telegram by app downloads. Throughout 2024, Indian users installed the messaging app approximately 100 million times. Russia ranked second, with over **** million downloads over the observed period, while U.S. users installed the app **** million times. Brazil recorded the largest number of downloads among Latin American countries. France and Germany were the leading markets in Western Europe. Telegram usage in India Telegram had the fourth-highest penetration rate among social media platforms in India, following WhatsApp, Instagram, and Facebook. In the third quarter of 2023, more than ** percent of Indian active social media users reported having used Telegram over the past month. However, user engagement on Telegram in India was significantly lower than on other major networks. On average, Indian users spent *** hours per month on the platform in 2021, which was ** times shorter than on WhatsApp. Growing popularity of Telegram As of July 2024, Telegram had *** million active users around the globe, having multiplied its audience tenfold since December 2014. Telegram recorded a sharp increase in global downloads in the first quarter of 2021, when WhatsApp, its competitor owned by Meta, updated its privacy policy. In March 2022, Telegram further increased its popularity in Russia after Meta platforms Facebook and Instagram were banned in the country. The number of daily active users (DAU) of Telegram nationwide grew by ** percent between January and March 2022.

More than one-third of Telegram users in Belarus started trusting the app's data privacy more over the past year, according to the 2021 survey. Approximately one-fifth of Ukrainians using the instant messaging app expressed increased trust during that time period, compared to 17 percent of users from Russia.

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".

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.

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.

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.

Nearly one-third of Telegram users in Russia were Moscow residents, as of 2023. Saint Petersburg accounted for the second-largest share of the instant messaging app users in the country, reaching about 12.6 percent.

Argentina Telegram: Delivered data was reported at 5,463,369.000 Unit in 2024. This records a decrease from the previous number of 7,302,042.000 Unit for 2023. Argentina Telegram: Delivered data is updated yearly, averaging 5,643,030.500 Unit from Dec 1999 (Median) to 2024, with 26 observations. The data reached an all-time high of 7,620,158.000 Unit in 1999 and a record low of 4,651,735.000 Unit in 2004. Argentina Telegram: Delivered data remains active status in CEIC and is reported by National Institute of Statistics and Censuses. The data is categorized under Global Database’s Argentina – Table AR.TB001: Telecommunication Statistics.

In the fourth quarter of 2024, Telegram generated 3.73 million downloads in Brazil, a relatively steady value since the second quarter of 2022, but less than half of its peak of 9.85 million downloads in the first quarter of 2021. Although, WhatsApp is used by more than 90 percent of Brazilian internet users, Telegram is now present in around 60 percent of the smartphones in the country.

Feelings of collective victimhood have been demonstrated to have a strong effect on ingroup bias, outgroup hostility and support for violence. The use of narratives stirring these feelings in far-right communications is especially concerning given their inclusion in the manifestos of several mass killers across Europe and North America. However, scholars still have little knowledge on the reach of such narratives as well as the extent to which major salient events increase attention to collective victimhood messaging among far-right followers. To address these gaps, we analyze the use of collective victimhood narratives on the popular secure instant messaging service, Telegram, which has exploded in popularity in response to mainstream platforms’ attempts to moderate extremist speech. We develop a supervised machine learning algorithm to predict the presence of these discourses in text from over 18.5 million messages that were extracted from 1,870 far-right Telegram channels. We then use these data to test what impact the George Floyd protests and the storming of the US Capitol had on the frequency of collective narrative discussions on far-right Telegram. Our findings suggest that both events coincided with a significant increase in the use of victimhood narratives, thus providing insight into the radicalization process of far-right communities online.

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.