A Topic-wise Exploration of the Telegram Group-verse
Alessandro Perlo Giordano Paoletti Nikhil Jha Luca Vassio Jussara Almeida Marco Mellia






  Politecnico di Torino
  Torino
  Italy

alessandro.perlo@studenti.polito.it



  Politecnico di Torino
  Torino
  Italy

giordano.paoletti@polito.it



  Politecnico di Torino
  Torino
  Italy

nikhil.jha@polito.it



  Politecnico di Torino
  Torino
  Italy

luca.vassio@polito.it



  Universidade Federal de Minas Gerais
  Belo Horizonte
  Minas Gerais
  Brazil

jussaramarquesdealmeida@gmail.com



  Politecnico di Torino
  Torino
  Italy

marco.mellia@polito.it









§ ABSTRACT 
Although currently one of the most popular instant messaging apps worldwide, Telegram has been largely understudied in the past years.

In this paper, we aim to address this gap by presenting an analysis of publicly accessible groups covering discussions encompassing different topics, as diverse as  Education,  Erotic, Politics, and Cryptocurrencies. We engineer and offer an open-source tool to automate the collection of messages from Telegram groups, a non-straightforward problem. We use it to collect more than 50 million messages from 669 groups. 

Here, we present a first-of-its-kind, per-topic analysis, contrasting the characteristics of the messages sent on the platform from different angles — the language, the presence of bots, the type and volume of shared media content. Our results confirm some anecdotal evidence, e.g., clues that Telegram is used to share possibly illicit content, and unveil some unexpected findings, e.g., the different sharing patterns of video and stickers in groups of different topics. While preliminary, we hope that our work paves the road for several avenues of future research on the understudied Telegram platform.



    Marco Mellia
     
================





§ INTRODUCTION


Telegram has experienced remarkable growth in the past years, becoming one of the most popular instant messaging apps in the world. In July 2023, it surpassed the mark of 800 million monthly active users worldwide<cit.>. Telegram offers several features to its users, who can organize themselves into different spaces of communication such as groups (many-to-many) or channels (one-to-many). 

Yet, the literature on Telegram is still limited in breadth. Targeting publicly accessible groups and channels, most prior works focused on textual content (e.g. news, hate speech), specific groups (e.g., terrorists <cit.>) or countries (e.g., Iran <cit.>), and often a single topic of discussion (e.g., far-right politics <cit.>).

In contrast, we are driven by the hypothesis that user activity patterns on Telegram groups may be influenced by the main topic of discussion. We aim to offer a broad analysis of the platform usage by providing a first-of-its-kind, multi-faceted, topic-wise exploration of usage patterns on popular Telegram groups.    


We use our crawler to gather data from more than a thousand open groups and select 669 of them with at least 100 active users, distributed across 10 different topics like Politics, Cryptocurrency, Video and Films, etc., where users discuss about some specific topic. In total our data covers around 51.6 M messages and 1.4 M distinct users over a two-month observation period.

We analyse our data aiming to precisely measure and contrast user activity patterns in groups across different topics, analysing the mix of various languages, the footprint of official Telegram bots, the diverse habits in sharing media (e.g., videos, audios, images, GIFs) and point to external content via URLs. We overall witness very different behavioural patterns; some are expected (e.g., the large usage of emojis in all topics and languages, or the sharing of possibly illicit content), others are more surprising (e.g., users in Darknet post much longer messages than users in the other topics; or videos published in Video and Films are much longer than those published in Erotic — the latter being with higher resolution).
 
Although preliminary, our results expose a very diverse universe. We believe that our work offers notable insights into the Telegram platform usage and constitutes a first step toward better understanding how users behave on such a platform.


§ RELATED WORK



The characteristics and dynamics of messaging platforms have attracted a lot of attention. Notably, prior studies analysed content properties <cit.> and information spread <cit.> on WhatsApp's groups, hinting at the catalytic role of the platform in various real-world events <cit.>.

More recently, attention has been dedicated to groups and channels on Telegram, as the platform's popularity increases across the globe. Some studies were interested in the inner workings of the mobile application <cit.>, and its use by particular user populations, such as  Iranian immigrants <cit.>, terrorist organizations <cit.>, extremist groups <cit.>,  or particular countries (e.g., Iran and Russia <cit.>). Others analysed the formation of communities within Telegram channels <cit.> and their connection to information spread <cit.>.  Some other efforts studied content properties, limiting to textual content, and usage patterns, with attention given to news content <cit.>, hate speech and abusive language <cit.>, as well as the presence of fake channels (i.e., those impersonating important services or persons) <cit.>. The use of Telegram to perform illicit activities (e.g., pump-and-dump activities in cryptocurrency markets <cit.>, manipulation of social media popularity <cit.>) has also been previously addressed. Overall, previous works focused on the information people exchange on Telegram, in groups and channels of a specific topic.
Only Morgia et al. <cit.> used TGStat to gather channels associated with multiple topics. Yet, they did not distinguish between such topics and aggregated all of them to discover fake channels.

In contrast, we here offer a topic-wise analysis of common user activities in Telegram groups. Rather than focusing on the type of exchanged information, or how it spreads, we show how differently people leverage different features (e.g.,  media types, links to external sites) to interact with each other when discussing different topics. 


§ CRAWLER AND DATA COLLECTION





Given our interest in user interactions, 

our data collection effort is focused on Telegram public groups, i.e., public chats where all the members can send messages. 



To collect the data, we design an open-source, two-stage crawler that we offer to the community.[The code and data will be made available upon publication of the paper.] 
At the first stage, the tool periodically crawls the TGStat website to discover public Telegram groups on various topics. At the second stage, the tool crawls Telegram, by joining the discovered groups and collecting all shared messages.






 §.§ TGStat crawling


TGStat is a mostly undocumented service that catalogues popular Telegram groups and channels worldwide. 

Currently, TGStat's database covers almost 1.9 M channels and groups <cit.>, which are categorised into 48 pre-defined topics.


For each topic, TGStat shows the lists of 
the top-100 groups/channels according to various metrics.  As the whole database, these lists are dynamically updated. 


Each group is characterised by some metadata, including the group name, its topic, its language, and the monthly Active Users (AU), i.e., the number of unique users who have written at least one message inside the group in the past month.













We extract information from TGStat engineering a Python-based crawler using the BeautifulSoup Python package <cit.>. We periodically run the crawler to automatically extract the lists of groups. 
This allows us to grow the group lists in those topics of interest to us (see discussion in Section <ref>).


Most prior studies of Telegram searched for links to existing groups in social media, news and even word-of-mouth <cit.>. Such approach potentially leads to strenuous crawling work, especially if one is interested in covering diverse topics (as we do here). TGStat offers on a single platform easy access to a large number of already categorised groups, with dynamically updated activity metrics and topics. Such information allows us to narrow our focus to the most relevant groups, besides enabling a per-topic analysis. Indeed, a few recent studies have used TGStat as a starting point to explore Telegram <cit.>. Yet, 
unlike these studies which gathered a single snapshot of listed groups, we here continuously monitor TGStat by growing the initial list of groups over multiple days. Moreover, given our interest in the per-topic analysis, we first estimate how reliable TGStat categorisation is, a neglected step in past works (see Section <ref>).



 §.§ Telegram crawling


Given a list of previously discovered groups, our crawler automatises the group join and message collection tasks. We rely on the Telethon Python package <cit.> and design a scalable tool based on threads: a master instructs workers to join (and leave — if desired) a group, check if a pending request for join has been accepted, collect new messages, or just wait. For scalability, we use multiple Telegram IDs, each associated with a worker.
The master maintains a list of groups to join 
and instruments workers to join these groups and collect all messages from a desired initial date until the present. We store the collected information in a MongoDB database for later processing.

We instrument our crawler to join and stay in groups discovered on TGStat. To refresh the collection of messages, workers download only the new messages since the last retrieved snapshot. 
For every group and message, the crawler stores all the returned information in JSON format in the MongoDB instance. In this paper, we focus on the following message information: sender user's identifier, message body, message time, and possible media contained in the message (image, video, GIF, poll, etc.).












 §.§ Crawler design challenges

Telegram implements several countermeasures to limit API abuse, notably:  i) a limit of 500 groups a given Telegram ID can join; ii) an unspecified upper limit on the rate to join new groups which, if not respected, 

causes a lengthy temporary ban<cit.>; 
iii) a without-any-notice permanent ban of novel-activated Telegram IDs. 
Respecting these limitations requires ingenuity when designing the crawler. First, we declared our intentions to the official Telegram support channel. 
Second, we carefully limited the group joining rate. Third, we used multiple Telegram IDs, each associated with a worker thread to scale the data gathering.

Telegram offers the possibility of setting up administration bots (known as Telegram bots) that ease group management. Captcha protection bots are popular for filtering fake user bots, i.e., actual Telegram accounts used to programmatically spam messages in open groups. Such captcha protection bots may kick users if they do not solve the captcha after a specific time. However, other bots or administrators might enforce different rules or criteria for group participation. Whenever we were removed from a group, we respected the administrators' willingness and did not try to join the group again. 

Similarly, to respect the privacy indications of the group administrators, we only consider groups where “auto-delete” functionality is not enabled.[The auto-delete setting erases messages for all participants either 24 hours or 7 days after sending. ]


Our crawler can collect up to thousands of messages per second per worker and join about tens of channels per hour without causing Telegram rate limitations.


§ TOPIC CHARACTERISATION







 §.§ Data collection and filtering
  
On April 1st, 2024 we 
collected 
the top-100 groups 
for all TGStat topics. Out of these, we select the subset of topics in which we are able to join at least 10 English language groups, a condition that allows us to manually validate the accuracy of TGStat's topic labelling.



  §.§.§ Topic selection
 Out of the 48 topics, 12 topics have 10 or more English groups. We keep crawling TGStat every week to refresh the lists of top-100 groups for these 12 topics to observe how those lists change over time. We stop on May 1st, 2024, discovering 1,368 groups in total. We notice the largest growth in Erotics, Cryptocurrencies and Bookmaking, where we find around 20% new groups. This illustrates that taking a single snapshot from TGStat, as prior work <cit.>, would limit the lists of discovered groups.

Feeding these growing lists to the Telegram crawler, 8.6% of groups have the auto-delete function enabled. We abandon them immediately. We instead fail to join 18.8% of groups because the group: (i) expired, or (ii) changed its name before we could join it, or (iii) is moderated and either the administrator did not admit us or a bot kicked us out after joining. We thus successfully join and monitor 993 groups overall. For each tracked group, we collect all messages starting from March 1st to April 30th.[For this work, we limit the collection period to avoid overloading the Telegram servers.] In total, we collect more than 50 M messages, with about 1 M new messages gathered each day.






  §.§.§ TGStat topic verification



As mentioned, we evaluate if the per-topic categorisation provided by TGstat is reliable. To that end, we check if the actual  topic of discussion is (i) coherent with the topic assigned by TGStat and (ii) consistent across time. We pick all 206 English groups. For each group, we select three sets of 30 consecutive messages, each set separated by the others by ten days. We split the 206 groups into three partitions (with TGStat's topics evenly distributed among partitions) and assign each to a human verifier who checks if the TGStat topic is consistent with the contents of the messages. In case of doubts, the verifier asks for the support of the other two. 


TGStat's topic assignment proves mostly correct, with two exceptions: the Courses and guides groups are mostly filled with spam; and the Economics groups mostly host discussions about cryptocurrencies, for which a dedicated topic already exists. We thus discard these two topics' remaining 166 groups, ending up with 827 groups.



  §.§.§ Selecting active groups

To guarantee groups are active and with enough diversity, we keep groups that have at least 100 active users, i.e. users who sent at least one message in the two-month observation period.
From 827 groups, we discard 158 of them, ending with 669 groups, as detailed in column 3 of Table <ref>. For comparison, the total numbers of groups discovered in TGStat in each topic is shown in column 2.

The table also details the average number of users per group, the percentage of active ones, the total number of messages and the average number of messages per active user.  Figures vary widely, showing already very different interests, engagement and activity levels across the topics.

For the sake of completeness, Figure <ref> in the Appendix <ref> details the breakdown of the various cases one can face when trying to collect messages from Telegram. Depending on the topic, we observe various failure cases which may significantly reduce the number of groups to follow. 




 §.§ Per-Topic Characterisation

We characterize our dataset by extracting various features from each group and then aggregating them on a per-topic basis (i.e., per-group macro average). This allows us to avoid the bias induced by large groups. 
Our goal is to explore how differently users interact on each topic.



  §.§.§ Telegram Bot usage and user activity



Telegram group administrators can incorporate bots into their groups. Bots offer a wide array of functionalities, from welcoming newcomers with group rules to responding to user commands, from collecting statistics to moderating messages.
Telegram bots are in fact quite popular: only 10.2% of groups in our dataset do not include bots; in the median, there are four bots per group. Interestingly, 1.5% of groups have 20 bots (the maximum allowed by Telegram). Some bots enjoy significant popularity: Combot<cit.> and MissRose_bot<cit.> are installed in 145 and in 129 groups, respectively. Both provide moderation services, analytics, and anti-spam features.

Bots footprint is not negligible: in groups with bots, they generate on average 8.6% of messages, with notable variations across topics. Figure <ref> shows, for 4 topics, the Empirical Complementary Cumulative Distribution Function (ECCDF) of the fraction of messages sent by bots for different groups. In Linguistics, bots generate the highest fraction of messages (brown dotted curve). In fact, some bots are integral to learning platforms and merit examinations. For instance Quizbot<cit.> is widely deployed in Linguistic and Education groups (27.5% and 28.6%, respectively). It generates an average of 39.0% and 13.0% of messages. In one Linguistics group it generates 91% of messages. Conversely, Politics groups see the smallest fraction of messages generated by bots (green dotted line), possibly testifying to a higher user engagement[For messages sent by a user account, we cannot distinguish between messages sent by a human or by an automated system.] in political groups than in other topics. 

Curiously, there is a quite large fraction of groups with bots that simply collect statistics or moderate the group without sending any message (leftmost part of Figure <ref>).  
For the remainder of our analysis, we ignore messages sent by Telegram bots.

Focusing on the amount of messages actual users generate, we observe that there are few users sending thousands of messages, while the majority are not active (see column 4 in Table <ref>) or send few messages. Indeed, the Empirical Probability Density Function (EPDF) of the number of messages generated by users follows a heavy-tailed shape that can be fit by a Pareto distribution with α=1.9.  Remarkably, the fittings of the per-topic EPDFs are very similar, hinting at the universality of these behaviours.



























  §.§.§ Language



The next question we answer is what are the languages people speak in each topic and group. For each textual message in a group, we associate a language by employing the FastText language identification library <cit.> to obtain the distribution of languages for each group.
In Figure <ref> we report the breakdown of the most popular language for groups on the same topic:

∙ English (a global language) and Russian (being Telegram very popular in Russia) are the two most popular languages.

Their share changes based on the topic. For instance, most groups in Bookmaking and Darknet have Russian messages. Conversely, the majority of Education and Cryptocurrency groups are in English, possibly due to the worldwide interest in such topics.



 ∙ Despite Telegram's restricted use in Iran, some popular groups are in Persian, especially in Politics. This is in line with the claim that Telegram is among the platforms to evade state censorship in Iran <cit.>.

 ∙ Telegram is blocked in China. In fact, it does not appear to be popular for discussing topics in Chinese. Yet, few groups have Chinese as the dominant language.


We observe that in almost half of the groups, more than 75% of the messages are written in the same dominant language. Still, in all groups and topics, we observe messages written in other languages, hinting to a global user population. 


Linguistics is the topic where groups contain the largest mix of languages, which supports the anecdotal observation of users practising foreign languages and mixing messages in their native language in such  

groups. In contrast, both Darknet and Technology stand out with more than 58%  
of the groups having more than 80% of the messages in a single language (English). This agrees with the intuition that technology-related discussions are carried over in English.



  §.§.§ Message length






Figure <ref> shows the ECCDF of the length of textual messages for each topic. Since message length may be influenced by language, we consider only messages written in English-dominated groups. We observe some great distinctions across topics. On one hand, Darknet groups are dominated by very long messages (80% longer than 100 characters).
A manual check unveils that most messages contain samples of illicit content people trade. The same can be said for Bookmaking and Technologies groups, where long messages describe bookmaking websites, experiences and results of betting, or devices to sell. In Erotic, people advertise their services.
Conversely, Linguistics and Politics groups are dominated by very short messages in which people debate or provide suggestions (80% shorter than 30 characters). In a nutshell, the presence of “advertisement” messages inflates their length, while discussion-driven groups see a predominance of short messages.

At last, steps in the distribution signal the presence of repeated automated messages (mostly spam).




  §.§.§ Usage of non-textual elements


We now broaden our analysis to consider non-textual elements. 

Specifically,  we extract, for each group, the fraction of messages containing images, external links, voice messages, polls, GIFs, stickers, videos, and emojis. How are these elements used? To gauge this, we compute the average usage fraction over all groups of a given topic (macro average). 
Figure <ref> visually compares two pairs of selected topics using radar charts. Table <ref> and Figure <ref> in Appendix <ref> provides the complete set of results. Some interesting findings emerge:

 ∙ Politics groups represent the typical average usage of non-textual elements: 20–30% of messages contains emojis; 10% of messages shares an image; stickers are more popular than GIFs; few messages contain voice content; polls are mostly an unused feature (present only in Linguistics groups).

 ∙ Cryptocurrencies  groups represent some mixed usage: no videos and voice messages, fewer photos and emojis but more stickers and GIFs.

 ∙ Groups in Video and Films  and  Politics have very similar usage patterns (i.e. radar shape), though, surprisingly, the former has fewer videos (present in only 1% of the messages) — see Section <ref>.



 ∙  
 Erotic groups present the minimum usage of non-textual elements: no stickers, no GIFs, while photos are found in 6% of messages. 
 Surprisingly, we see very few links to external platforms. Indeed, readers are invited to contact “advertisers” via private chat.


Notice that sending stickers requires manual actions hardly automatizable. 
Also, stickers are commonly used as reactions to other messages. Their prominent use in a group or topic may testify to a larger fraction of messages being sent by real users, or to a more confidential exchange: Erotic, Darknet and Technologies have the least fraction of stickers and are dominated by ad-style messages. Conversely, in Bookmaking and Cryptocurrencies people use more stickers as reactions for suggestions.















§ MULTIMEDIA AND EXTERNAL LINKS



We now delve deeper into the usage of specific non-textual elements, notably shared videos and URLs to external sites.



 §.§ Video size and duration

We focus our analysis on groups in the three topics with the largest share of videos: Politics, Video and Films, and Erotic. Although  Figure <ref> suggests some similarities in the amount of shared videos, a closer examination of the video duration and file size reveals noteworthy differences in goals and types of shared videos. 

The left plot of Figure <ref> compares the video duration. Observe how videos shared in Video and Films are notably longer than in other topics,

with peaks roughly around the 60- and 120-minute marks (notice the log-y scale). Manual inspection confirms that people share entire TV series episodes and movies. Their total volume amounts to 7.45 TB of data.

A comparison of video duration with file size — see the right graph of Figure <ref> — exposes an noticeable difference between topics: videos shared in Erotic groups have significantly higher video rate (thus quality), as evidenced by the steeper slope of the regression line.



















 §.§ External URL lookup



Analyzing the sharing of links to external websites (i.e., different from <telegram.me> and <t.me>) allows us to understand 

how Telegram users may be redirecting (or driving) attention (and traffic) to other websites.





In Figure <ref>, we present the average frequency at which a domain appears in a given topic. We focus on the union of the 5 most popular domains across each topic. These collect from ≈20% to ≈50% of links — with a fragmented list of other platforms (bottom row).
The three most frequent platform are social networks: X, YouTube and Instagram. Usage varies a lot: X sees significant use in Cryptocurrency; YouTube and, to a lesser extent, Instagram are the two most transversal platforms. Conversely, in line with their discussion topics, Technology and Software and Application show a significant usage of GitHub pages. The <telegra.ph> open and anonymous publishing platform is used in Software & Applications, e.g. to share installation guides and tutorials. 

    




§ CONCLUSION



In this paper, we offered a first-of-its-kind transversal analysis of Telegram as observed through the lens of public groups retrieved from TGstat. 
We observed a large diversity of the usage that communities do of the platform capabilities, which in turn reflects the type of content and goals of sharing: for instance, bots may have a huge footprint — with up to 80–90% of  bot-generated messages. User messages length varies a lot — with the self-advertisement-driven content inflating message length; video sharing is quite common — in Politics these are short, while in Video and films entire movies get shared — Erotic having the highest quality.

All in all, our work, albeit preliminary, paves the road to the investigations and understanding of how people use the Telegram platform.
We hope our work can foster discussion towards a deeper understanding of this platform.







§ ETHICS


In our work, we take ethics under utmost consideration throughout the whole process.

In the first place, while crawling TGStat, we respect the page visit rate imposed by the website itself. We keep our data extraction rate (few pages per minute) at a level to minimise TGStat's load. For the same reason, we repeat the crawling only once a week.

Monitoring Telegram groups can also be delicate. First of all, we checked the privacy policy of the Telegram platform. It does not forbid crawling. We also asked explicitly Telegram's support at support@telegram.org and security@telegram.org to declare our intention, asking them to share with us any restrictions or limitations. We received no answer.

To respect privacy restrictions imposed by users and administrators, we restrict our analysis to public groups where it is common knowledge and well-accepted that anybody with a link to access the chat can participate as a member — if not as an active user. We respect both the groups where the administrator sets the auto-delete functionality and those where admins refuse — or ignore — our join request. We do not monitor nor store any data about these groups.

Considering users participating in groups, we store only their TelegramID (which are randomly generated), and do not store any Personally Identifiable Information such as usernames (which are in any case freely chosen by the users and can change at any time) or profile pictures. Usernames can appear in the message content when a user is mentioned by someone else. We did not make any effort to re-identify any users.

Finally, we are aware that Telegram is sometimes used to share copyright-protected material and illicit content — more so, our results suggest that this behaviour is frequent. To prevent being implicated in such activity, we avoid downloading and storing any actual media such as pictures and videos, only collecting metadata (e.g., video duration and size).






§ APPENDIX



In the appendix, we include some secondary results that can help the reader have a more complete understanding of the context of our analysis.

In Figure <ref>, we show the breakdown of the groups we found on TGStat, and those we actually monitor for the paper.

In Table <ref>, we provide a qualitative explanation of the topics under observation.

In Table <ref>, we present per-topic detailed quantities.



















ACM-Reference-Format


