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 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.

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.

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.

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

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

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.

During a survey conducted in early 2025, 19 percent of respondents who had Telegram installed on their phones said they used the app every day, down from 30 percent a year earlier. Meanwhile, 22 percent of interviewees used Telegram a few times per week. The number of Telegram users worldwide grew almost four times between 2018 and 2022.

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

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.

More than ** percent of Telegram users in Belarus resided in Minsk Oblast as of 2021. Brest Oblast held the second largest share of the instant messaging app users in the country, at around *** percent.

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.

During the fourth quarter of 2024, mobile messaging market leader WhatsApp reported around 1.13 million downloads from users in the United Kingdom. By comparison, Telegram reported around 1.22 million app downloads during the same period, while instant messaging platform Signal reported approximately 388 thousand app downloads by UK users. Between 2020 and the end of 2023, WhatsApp has consistently amassed more app downloads than competitors Telegram and Signal, but Telegram gained ground among UK users in 2024, with almost two million downloads during the second quarter of the year, compared to the 1.11 million downloads recorded by WhatsApp during the same period.

The instant messaging (IM) tool market is experiencing robust growth, driven by the increasing penetration of smartphones, affordable data plans, and the rising demand for real-time communication. The market, estimated at $100 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $300 billion by 2033. This growth is fueled by several key trends, including the integration of IM functionalities into various platforms (e.g., social media, e-commerce), the rise of business messaging applications, and the increasing adoption of encrypted messaging services prioritizing user privacy. The market is segmented by various factors such as platform (mobile, desktop, web), messaging type (text, voice, video), and user base (consumer, enterprise). Leading players like Facebook (WhatsApp), Apple (iMessage), Google (Google Chat), and others are constantly innovating to enhance features, improve security, and expand their user base, leading to a highly competitive landscape. Market restraints include concerns around data privacy and security breaches, the prevalence of misinformation and fake news spread through IM platforms, and regulatory challenges related to content moderation. Despite these challenges, the market's growth trajectory remains positive, driven by continued technological advancements, the increasing adoption of cloud-based messaging solutions, and the expanding global internet penetration. The emergence of new features like augmented reality (AR) and virtual reality (VR) integration within IM platforms will further drive market expansion in the coming years. The regional distribution shows a strong presence in North America and Asia-Pacific, with developing economies witnessing rapid growth due to rising smartphone adoption and internet accessibility. Companies are also focusing on developing niche applications for specific industries and sectors, further diversifying the market landscape.

MultiSocial is a dataset (described in a paper) for multilingual (22 languages) machine-generated text detection benchmark in social-media domain (5 platforms). It contains 472,097 texts, of which about 58k are human-written and approximately the same amount is generated by each of 7 multilingual large language models by using 3 iterations of paraphrasing. The dataset has been anonymized to minimize amount of sensitive data by hiding email addresses, usernames, and phone numbers.

If you use this dataset in any publication, project, tool or in any other form, please, cite the a paper.

Disclaimer

Due to data source (described below), the dataset may contain harmful, disinformation, or offensive content. Based on a multilingual toxicity detector, about 8% of the text samples are probably toxic (from 5% in WhatsApp to 10% in Twitter). Although we have used data sources of older date (lower probability to include machine-generated texts), the labeling (of human-written text) might not be 100% accurate. The anonymization procedure might not successfully hiden all the sensitive/personal content; thus, use the data cautiously (if feeling affected by such content, report the found issues in this regard to dpo[at]kinit.sk). The intended use if for non-commercial research purpose only.

Data Source

The human-written part consists of a pseudo-randomly selected subset of social media posts from 6 publicly available datasets:

1. Telegram data originated in Pushshift Telegram, containing 317M messages (Baumgartner et al., 2020). It contains messages from 27k+ channels. The collection started with a set of right-wing extremist and cryptocurrency channels (about 300 in total) and was expanded based on occurrence of forwarded messages from other channels. In the end, it thus contains a wide variety of topics and societal movements reflecting the data collection time.

2. Twitter data originated in CLEF2022-CheckThat! Task 1, containing 34k tweets on COVID-19 and politics (Nakov et al., 2022, combined with Sentiment140, containing 1.6M tweets on various topics (Go et al., 2009).

3. Gab data originated in the dataset containing 22M posts from Gab social network. The authors of the dataset (Zannettou et al., 2018) found out that “Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls.” They also found out that hate speech is much more prevalent there compared to Twitter, but lower than 4chan's Politically Incorrect board.

4. Discord data originated in Discord-Data, containing 51M messages. This is a long-context, anonymized, clean, multi-turn and single-turn conversational dataset based on Discord data scraped from a large variety of servers, big and small. According to the dataset authors, it contains around 0.1% of potentially toxic comments (based on the applied heuristic/classifier).

5. WhatsApp data originated in whatsapp-public-groups, containing 300k messages (Garimella & Tyson, 2018). The public dataset contains the anonymised data, collected for around 5 months from around 178 groups. Original messages were made available to us on request to dataset authors for research purposes.

From these datasets, we have pseudo-randomly sampled up to 1300 texts (up to 300 for test split and the remaining up to 1000 for train split if available) for each of the selected 22 languages (using a combination of automated approaches to detect the language) and platform. This process resulted in 61,592 human-written texts, which were further filtered out based on occurrence of some characters or their length, resulting in about 58k human-written texts.

The machine-generated part contains texts generated by 7 LLMs (Aya-101, Gemini-1.0-pro, GPT-3.5-Turbo-0125, Mistral-7B-Instruct-v0.2, opt-iml-max-30b, v5-Eagle-7B-HF, vicuna-13b). All these models were self-hosted except for GPT and Gemini, where we used the publicly available APIs. We generated the texts using 3 paraphrases of the original human-written data and then preprocessed the generated texts (filtered out cases when the generation obviously failed).

The dataset has the following fields:

• 'text' - a text sample,

• 'label' - 0 for human-written text, 1 for machine-generated text,

• 'multi_label' - a string representing a large language model that generated the text or the string "human" representing a human-written text,

• 'split' - a string identifying train or test split of the dataset for the purpose of training and evaluation respectively,

• 'language' - the ISO 639-1 language code identifying the detected language of the given text,

• 'length' - word count of the given text,

• 'source' - a string identifying the source dataset / platform of the given text,

• 'potential_noise' - 0 for text without identified noise, 1 for text with potential noise.

ToDo Statistics (under construction)

The global mobile voice social application market is experiencing robust growth, driven by the increasing adoption of smartphones, readily available high-speed internet, and the ever-growing demand for instant and convenient communication. The market, estimated at $150 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $500 billion by 2033. This growth is fueled by several key factors, including the rising popularity of voice-based social interactions, the integration of advanced features like voice-to-text and real-time translation, and the emergence of innovative applications catering to niche user groups. The market is highly competitive, with established players like WhatsApp, Facebook Messenger, and WeChat dominating the landscape alongside emerging players constantly innovating to gain market share. The integration of AI-powered features like voice assistants and personalized recommendations is further enhancing user experience and driving market expansion. Regional variations exist, with North America and Asia-Pacific currently leading the market due to high smartphone penetration and digital literacy. Significant market trends include the increasing preference for encrypted messaging, the integration of augmented reality (AR) and virtual reality (VR) features for enhanced social interactions, and the growing demand for voice-based social commerce. However, challenges such as data privacy concerns, the potential for misuse of voice technology, and the need to overcome language barriers continue to influence market dynamics. Furthermore, regulatory hurdles in certain regions and the potential for technological disruptions from newer platforms pose restraints on overall growth. Market segmentation is primarily based on application type (text-based, voice-based, video-based), operating system (Android, iOS), and user demographics (age, location, interests). The competitive landscape necessitates continuous innovation, strategic partnerships, and aggressive marketing efforts to maintain a competitive edge.

According to a 2024 survey, around ** percent of Telegram users in Brazil lived in large cities, with populations between 100 thousand and one million inhabitants. Another ** percent of the social platform users lived in megacities with over * million inhabitants, while medium-sized municipalities concentrated around ** percent of users. Overall, cities with populations over * million accounted for ** percent of the app user base in the country.

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.

The Sartorius Scale Monitor software has three goals. First, the software communicates with Sartorius scales that support the SBI protocol to query and convert the scale values in real-time, monitoring the calculated values and triggering an alarm in case of an unexpected event. The second goal is to provide the user with a web-based interface that provides alarm settings and an overview of the measured and calculated values. The third goal deals with notifying the user about alarms that occur. Here, a Telegram bot was integrated that provides all user information and interactions in a group channel. The bot can inform the users about the current status, as well as about occurring alarms, in addition, simple user interaction with the bot is possible to set the alarms on or off. The integration with the Telegram bot requires an existing bot and a Telegram channel.