As of July 2024, cloud-based mobile messaging app Telegram reported 950 million 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 by users aged between 25 and 34 years. Among the reasons to use Telegram, 80 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 167 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).

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.

As of January 2025, monthly active users of Telegram accounted for over 11 percent of the global population, marking an increase from the previous year. In total, 950 million people used Telegram on average per month.

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.

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.

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.

This dataset offers a comprehensive collection of Telegram users' geolocation data, including IP addresses, with full user consent, covering 50,000 records. This data is specifically tailored for use in AI, ML, DL, and LLM models, as well as applications requiring Geographic Data and Social Media Data. The dataset provides critical geospatial information, making it a valuable resource for developing location-based services, targeted marketing strategies, and more.

What Makes This Data Unique? This dataset is unique due to its focus on geolocation data tied to Telegram users, a platform with a global user base. It includes IP to Geolocation Data, offering precise geospatial insights that are essential for accurate geographic analysis. The inclusion of user consent ensures that the data is ethically sourced and legally compliant. The dataset's broad coverage across various regions makes it particularly valuable for AI and machine learning models that require diverse, real-world data inputs.

Data Sourcing: The data is collected through a network of in-app tasks across different mini-apps within Telegram. Users participate in these tasks voluntarily, providing explicit consent to share their geolocation and IP information. The data is collected in real-time, capturing accurate geospatial details as users interact with various Telegram mini-apps. This method of data collection ensures that the information is both relevant and up-to-date, making it highly valuable for applications that require current location data.

Primary Use-Cases: This dataset is highly versatile and can be applied across multiple categories, including:

IP to Geolocation Data: The dataset provides precise mapping of IP addresses to geographical locations, making it ideal for applications that require accurate geolocation services. Geographic Data: The geospatial information contained in the dataset supports a wide range of geographic analysis, including regional behavior studies and location-based service optimization. Social Media Data: The dataset's integration with Telegram users' activities provides insights into social media behaviors across different regions, enhancing social media analytics and targeted marketing. Large Language Model (LLM) Data: The geolocation data can be used to train LLMs to better understand and generate content that is contextually relevant to specific regions. Deep Learning (DL) Data: The dataset is ideal for training deep learning models that require accurate and diverse geospatial inputs, such as those used in autonomous systems and advanced geographic analytics. Integration with Broader Data Offering: This geolocation dataset is a valuable addition to the broader data offerings from FileMarket. It can be combined with other datasets, such as web browsing behavior or social media activity data, to create comprehensive AI models that provide deep insights into user behaviors across different contexts. Whether used independently or as part of a larger data strategy, this dataset offers unique value for developers and data scientists focused on enhancing their models with precise, consented geospatial data.

The largest share of Telegram users as of 2023 was between 25 and 34 years of age, at over 29 percent. Users of the messaging app aged younger than 24 years accounted for over 25 percent of the user base.

The Pushshift Telegram dataset is made up of over 27.8K channels and 317M messages from 2.2M unique users. The Pushshift Telegram dataset can help researchers from a variety of disciplines interested in studying online social movements, protests, political extremism, and disinformation.

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.

According to Cognitive Market Research, the global Mobile Messaging Service market size will be USD 102584.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 16.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 41033.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 30775.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 23594.37 million in 2024 and will grow at a compound annual growth rate (CAGR) of 18.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 5129.21 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 2051.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.9% from 2024 to 2031.
The software category is the fastest growing segment of the Mobile Messaging Service industry

Market Dynamics of Mobile Messaging Service Market

Key Drivers for Mobile Messaging Service Market

Increased adoption of smartphones and mobile internet drives the market growth 

As more people around the world gain access to affordable smartphones and mobile data, they increasingly turn to mobile messaging apps (like WhatsApp, Telegram, WeChat, or Signal) for daily communication. These apps often offer free, instant, and multimedia-rich messaging, which is especially appealing in regions where traditional SMS or calling may be expensive or unreliable. For instance, in India, the rapid rise in smartphone usage and low-cost internet provided by companies like Reliance Jio led to an explosion in WhatsApp users. By 2023, WhatsApp had over 500 million users in India alone, becoming the country’s primary communication platform for personal, business, and even government messaging services.

The Global Proliferation of Smartphone Usage to Boost Market Growth

The market for mobile messaging services is expanding due in large part to the increase in smartphone adoption. Mobile messaging services are becoming more widely available to more people as cell phones are cheaper and easier to obtain, especially in developing nations. The increasing number of users on messaging apps like WeChat, Telegram, and WhatsApp is a result of their widespread acceptance, which raises user engagement. Additionally, smartphones have improved features like voice, video, and multimedia messaging, which boost communication in general. Furthermore, messaging apps are made more user-friendly and effective by the advancement of sophisticated smartphone technology like fast internet and potent CPUs. As a result, the market for mobile messaging is growing as a direct result of rising smartphone usage worldwide.

Growing Need for Instant Communication to Drive Market Growth

The mobile messaging service market is growing due in large part to consumer demand for instant communication. A growing number of people and businesses are turning to messaging apps like WhatsApp, Messenger, and Telegram because they need rapid, effective, and real-time communication solutions in today's hectic world. Instantaneous phone or video calls, message exchanges, and material sharing are all made possible by these technologies, which improve communication efficiency and speed. Instant messaging is being used more and more by organizations as a flexible means of communication since it offers an efficient avenue for teamwork, marketing, and customer service. More market expansion for mobile messaging services is being driven by consumers' desire for instant communication and the ease of being able to access them on their phones.

Restraint Factor for the Mobile Messaging Service Market

Data Privacy and Security Concerns Will Limit Market Growth

The market for mobile messaging services is being held back by serious worries about security and privacy. Users are becoming more and more concerned about data breaches, unlawful access, and the exploitation of their data as messaging platforms handle sensitive personal and corporate ...

The messenger app Telegram had 12 million paying users around the world as of December 2024, seven million more than in January of that year. Telegram introduced a freemium model with paid subscriptions in June 2022.

This dataset comprises 195,567 message IDs shared by 6,802 unique Telegram users on 25 pro-Bolsonaro public Telegram groups during August 2022, when the presidential campaign for the 2022 Brazilian elections started.

The message ID provided by Telegram API is unique only for the group or channel where it was posted. For this reason, the dataset provides the message ID and the group username (@).

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

More than 80 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 6.4 percent.

Russia had the largest share of internet users aged 16 to 64 years who used Telegram among European countries, at over 64 percent in the third quarter of 2022. In Turkey, every second internet user was a Telegram customer.

MentalRiskES is a new dataset about mental disorders in Spanish. The dataset is divided into three distinct mental disorders:

• Eating Disorder
• Depression
• Anxiety

Each dataset contains a set of subjects and their message thread in a Telegram social network chat.

How is constructed?
Public groups on the Telegram social network were accessed, and conversations were extracted from them. This data was processed, and we kept only the text messages, excluding images, audio, etc. In order to carry out the annotation, a subset of messages was extracted from each subject. This message thread was annotated by 10 different annotators through the Prolific platform and made use of the Doccano annotation platform.

In this way, we associated a user ID with some tags that emerged after averaging the annotators' decisions. The labels available for each set are:

• Eating Disorder: suffer (s), control (c)
• Depression: suffer + in favour (sf), suffer + against (sa), suffer + other (so), control (c)
• Anxiety: suffer (s), control (c)

Labels
The values available in Anxiety files are:

• bs (binary suffer): 1 if the subject suffers and 0 if not according to the frequency of the labels (in case of a tie it is marked as suffers)
• bc (binary control): 1 if the subject does not suffer and 0 if they do according to the frequency of the labels (in case of a tie it is marked as suffers)
• rbs (regression binary suffer): number of times the subject has been marked as suffering among the total number of scorers, i.e., 10
• rbc (regression binary control): number of times the subject has been marked as not suffering among the total number of scorers, i.e., 10

The values available in the Depression and Eating Disorders files are:

• bs (binary suffer): 1 if the subject suffers and 0 if not, according to the frequency of the labels (in case of a tie it is marked as suffers)
• bsf (binary suffer favour): 1 if the subject suffers and is in favour,r and 0 if not according to the frequency of the labels
• bsa (binary suffer against): 1 if the subject suffers and is against, and 0 if not according to the frequency of the labels
• bso (binary suffer other): 1 if the subject suffers and is neither in favour nor against and 0 if not according to the frequency of the labels
• bc (binary control): 1 if the subject does not suffer and 0 if they do according to the frequency of the labels (in case of a tie it is marked as suffers)
• rbs (regression binary suffer): number of times the subject has been marked as suffering among the total number of scorers, i.e., 10
• rbc (regression binary control): number of times the subject has been marked as not suffering among the total number of scorers, i.e., 10
• rsf (regression suffer favour): number of times the subject has been marked as suffering and in favour among the total number of scorers, i.e., 10
• rsa (regression suffer against): number of times the subject has been marked as suffering and against and in favour among the total number of scorers, i.e., 10
• rso (regression suffer other): number of times the subject has been marked as suffering and is neither in favour nor against among the total number of scorers, i.e., 10
• rc (regression control): number of times the subject has been marked as not suffering among the total number of scorers, i.e., 10 (Note that it is equal to 'rbc')
  So, the labels 'rbs' and 'rbc' must sum to 1, and the labels 'rsf','rsa', 'rso' and 'rc' must sum to 1 too.

Preprocessing
The same corpus is found with emojis or without emojis; that is to say, in the folder 'processed' is the corpus with emojis in text format, while in the folder 'raw' is the corpus with emojis in original format.

MentalRiskES evaluation campaign
MentalRiskES is a shared task organized at IberLEF. The aim of this task is to promote the early detection of mental risk disorders in Spanish. In this task we made use of the corpusMentalRiskES, the partitions used are available in the folder MentalRiskES2023edition.zip provided in git (https://github.com/sinai-uja/corpusMentalRiskES). To cite the task: Mármol-Romero, A. M., Moreno-Muñoz, A., Plaza-del-Arco, F. M., Molina-González, M. D., Martín-Valdivia, M. T., Ureña-López, L. A., & Montejo-Raéz, A. (2023). Overview of MentalriskES at IberLEF 2023: Early Detection of Mental Disorders Risk in Spanish. Procesamiento del Lenguaje Natural, 71, 329-350.

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.

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.