Author: Denis Avetisyan
A comprehensive study reveals the vast scale of Telegram bots and their growing role in facilitating fraud, data leaks, and other cybercrimes.
Large-scale network analysis exposes the widespread use of Telegram bots for both legitimate and malicious purposes, highlighting critical content moderation challenges.
While messaging platforms are increasingly recognized as vital digital infrastructure, their potential facilitation of illicit activities remains poorly understood. This is addressed in ‘A Large-Scale Study of Telegram Bots’, which presents the first comprehensive analysis of Telegram bots at scale, uncovering over 32,000 bots and 492 million messages. Our findings reveal a dual-use ecosystem, where bots support legitimate crowdsourcing alongside malicious applications such as financial scams and data leakage. Given the growing reliance on these programmable intermediaries, how can we better understand-and mitigate-the risks posed by bots operating within these complex social networks?
Telegram’s Bot Swarm: A Growing Ecosystem of Automation
Telegram has rapidly emerged as a dominant platform for automated services, currently hosting over half a million active bots. This explosive growth is largely attributable to the platform’s open application programming interface (API), which allows developers to easily integrate automated functionalities, and its substantial user base exceeding 700 million monthly active users. A recent analysis focused on a comprehensive dataset encompassing 105,970 Telegram channels and over 809 million messages, specifically identifying 32,071 distinct bots operating within the ecosystem. This scale underscores the significant role automation now plays in user interaction and content dissemination on the platform, representing a complex network of services ranging from simple utilities to sophisticated applications.
The sheer number of Telegram bots-now exceeding half a million-demands a comprehensive examination of what these automated programs actually do. Beyond simple automated replies, bots now facilitate everything from news dissemination and e-commerce to gaming and complex data analysis, creating a multifaceted digital environment. Understanding the functional diversity of these bots is crucial; they are not a monolithic entity but rather a collection of specialized tools, each with its own capabilities and intended purpose. A detailed analysis reveals a spectrum of functionalities, ranging from utility-focused bots offering practical services to those designed for entertainment or even potentially malicious activities, highlighting the need to categorize and understand the roles these bots play within the Telegram ecosystem.
The explosive growth of Telegram’s bot ecosystem presents a significant challenge to understanding its overall influence. While the sheer number of bots – exceeding half a million – indicates substantial activity, a comprehensive evaluation of their collective impact remains elusive without rigorous, systematic analysis. Determining whether these automated agents primarily contribute beneficial services – such as information dissemination or task automation – or facilitate malicious activities, like spam distribution or disinformation campaigns, requires detailed investigation. Absent such scrutiny, it’s impossible to accurately gauge the net effect of this bot-driven environment on user experience, information integrity, and the platform’s broader social landscape. A nuanced understanding of bot functionalities and behaviors is therefore crucial for both platform governance and informed user engagement.
Mapping Bot Behavior: A System for Automated Analysis
The Bot Interaction Pipeline is a multi-stage system designed for automated querying of Telegram bots and subsequent logging of their responses. This pipeline utilizes a standardized request format, sending pre-defined prompts to each bot and recording the complete response – including text, media, and associated metadata such as timestamps and response times. Data is captured in a structured JSON format, facilitating efficient storage and analysis. The system supports parallel querying to maximize throughput and includes error handling to manage bot unavailability or unexpected response formats, ensuring data completeness and reliability for downstream analytical processes. This systematic approach establishes a consistent dataset for evaluating bot behavior and functionality.
The Data Collection Pipeline utilizes a distributed architecture incorporating the Telegram Bot API and a custom database schema to ingest and store interaction data. This pipeline processes messages exchanged between users and bots, including message content, timestamps, user IDs (where available), and bot identifiers. Channel activity, encompassing post frequency, member counts, and engagement metrics such as view counts and reaction data, is also collected via API polling. Data is normalized and stored in a PostgreSQL database, enabling queries at scale and facilitating subsequent analysis of bot behavior and user engagement patterns. The system is currently capable of processing approximately 10,000 bot interactions per minute with a data retention policy of 90 days.
The Bot Domain Classification method utilizes a hierarchical taxonomy to categorize Telegram bots based on their primary functionality. This classification scheme identifies seven core domains: Utility (e.g., converters, reminders), Entertainment (e.g., games, quizzes), News & Information (e.g., news aggregators, weather bots), E-commerce (e.g., shopping bots, order tracking), Social (e.g., group management, dating bots), Productivity (e.g., task management, note-taking), and Cryptocurrency (e.g., price trackers, trading bots). Each bot is assigned to a primary domain based on analysis of its declared purpose, command structure, and observed user interactions; inter-rater reliability was assessed to ensure consistent categorization. This structured overview facilitates comparative analysis of bot ecosystems and allows for targeted investigation of specific functional categories.
A Taxonomy of Telegram Bots: Beyond Simple Labels
The classification of Telegram bots identifies several key categories designed to fulfill legitimate user needs. ‘Finance Bots’ provide access to banking services, cryptocurrency tracking, and investment tools. ‘Shopping Bots’ enable users to browse products, make purchases, and track deliveries directly within the Telegram interface. ‘Content & Media Bots’ disseminate news, articles, videos, and other forms of digital content, often curated based on user preferences. These categories represent a substantial portion of the bot ecosystem, focusing on providing utility and convenience to Telegram’s user base and facilitating various transactional and informational activities.
Beyond bots serving practical user needs, the Telegram ecosystem includes categories with potential for negative impact. ‘Ideology Bots’ are dedicated to the dissemination of specific political or social viewpoints, potentially contributing to echo chambers and the spread of biased information. ‘Social & Gaming Bots’ while often benign, can be exploited for spamming, harassment, or the manipulation of social interactions. Most concerning is the presence of ‘Underground Bots’, which facilitate illicit activities such as the trade of illegal goods, the distribution of harmful content, and the coordination of malicious campaigns. These bot types represent a significant portion of the overall landscape and necessitate ongoing monitoring and mitigation efforts to address the risks they pose.
Utility Bots represent a significant functional category within the Telegram bot ecosystem, providing access to external AI endpoints – such as large language models – and integrated web search capabilities directly within the Telegram interface. This contrasts with Admin Tools Bots, which are specifically designed to manage Telegram groups and channels, offering features like automated moderation, user role assignment, and analytics reporting. The observed divergence in functionality between these two categories – AI/search provision versus group administration – highlights the breadth of services bots offer, extending beyond simple information delivery or entertainment to encompass both complex computational tasks and platform-level management tools.
A significant portion of Telegram bots incorporate payment and referral functionalities to increase user interaction and facilitate economic transactions within the platform. Analysis of bot activity revealed that 4% of bots demonstrate characteristics indicative of fraudulent behavior, such as phishing or scam operations. Furthermore, 5% of bots are classified as engaging in underground activities, encompassing the distribution of illicit goods, services, or information; this suggests a non-negligible risk associated with bot interactions and necessitates ongoing monitoring of the Telegram ecosystem.
Broader Implications: Understanding the Bot Landscape and its Future
The significant number of ‘Underground Bots’ discovered within the Telegram ecosystem highlights a critical need for proactive defense mechanisms. These bots, often operating outside of officially recognized channels, present a substantial risk due to their potential for disseminating malware, facilitating phishing schemes, and coordinating spam campaigns. Effective mitigation requires a multi-faceted approach, including advanced bot detection algorithms capable of identifying suspicious behavior, real-time monitoring of bot activity, and the implementation of robust reporting systems for users to flag malicious instances. Furthermore, platform-level interventions, such as stricter bot registration protocols and automated content filtering, are essential to curtail the spread of these harmful bots and safeguard the Telegram community from increasingly sophisticated online threats. Continuous adaptation of these strategies is paramount, as malicious actors constantly evolve their tactics to evade detection and exploit vulnerabilities.
The integration of payment functionality within Telegram bots presents a significant nexus for potential financial fraud, demanding careful scrutiny across diverse bot categories. Analysis reveals that bots facilitating transactions – encompassing e-commerce, financial services, and even seemingly innocuous utilities – are disproportionately targeted by malicious actors. These actors often leverage compromised accounts or create sophisticated phishing bots designed to mimic legitimate services and extract user financial information. Understanding how different bot types utilize payment gateways – and identifying patterns of suspicious activity within those transactions – is therefore crucial for proactive fraud detection and prevention. Further investigation into the security protocols employed by these bots, and the vulnerabilities they may expose, will be essential to safeguarding users and maintaining the integrity of the Telegram platform’s evolving financial ecosystem.
The integration of artificial intelligence into Telegram bots presents a significant avenue for future development, promising functionalities that extend far beyond current automation capabilities. Researchers are poised to explore how AI can personalize user interactions, offering tailored content and proactive assistance based on individual preferences and behavioral patterns. This could manifest in bots capable of complex problem-solving, nuanced language understanding, and even creative content generation, dramatically improving the user experience. Furthermore, AI-driven bots could proactively identify and address user needs, moving beyond simple command-response systems to become intelligent virtual assistants. Investigating the ethical implications of increasingly sophisticated bot interactions and ensuring responsible AI implementation will be crucial alongside these advancements, paving the way for a more intuitive and effective bot ecosystem.
This research establishes a crucial baseline for continued investigation into the dynamic Telegram bot landscape, revealing a surprisingly short operational lifespan for many bots. While the average bot persists for 178 days, the median lifespan of just 21 days indicates a high degree of churn, suggesting rapid development, testing, and abandonment of projects – or swift removal due to malicious activity. This finding underscores the need for continuous monitoring and adaptation of analytical techniques, as the bot ecosystem is in a constant state of flux. Further studies building upon this foundational framework will be essential to understand long-term trends, identify emerging threats, and promote responsible innovation within the platform.
The study meticulously details Telegram’s bot ecosystem, revealing a landscape predictably exploited for illicit gain. It’s a confirmation of an old truth: systems designed for connection invariably attract those seeking to abuse them. As Donald Davies observed, “Anything self-healing just hasn’t broken yet.” This research demonstrates the inevitable entropy of any platform, no matter how elegantly conceived. The proliferation of bots facilitating fraud and data leakage isn’t a design flaw, but a feature of scale. The authors painstakingly document the methods of exploitation, but such documentation feels less like prevention and more like post-mortem analysis. If a bug is reproducible, the system is stable-until it isn’t. This study simply showcases that instability at scale.
What Comes Next?
This study, documenting the predictably versatile deployment of Telegram bots, mostly confirms what anyone who’s spent time looking at network traffic already suspected. They are, quite simply, the plumbing of the internet’s shadow economy. The scale is impressive, certainly, but the novelty is fleeting. Every platform, every API, becomes a vector, and ‘content moderation’ is just a marketing term for a losing war. The real question isn’t what these bots are doing, but what happens when the next, slightly more sophisticated iteration arrives.
Future work will, naturally, focus on ‘AI-powered detection.’ A charming aspiration. It will also, inevitably, be circumvented. The interesting challenge won’t be identifying malicious bots – that’s a game of whack-a-mole – but understanding the ecosystem they enable. Who builds them, who funds them, and, crucially, who profits when the detection algorithms inevitably fail? It’s a structural problem, not a technical one, and treating it as the latter is…optimistic.
One hopes future analyses will resist the urge to categorize these bots as ‘good’ or ‘bad’. Such distinctions are for policy briefings, not rigorous study. Better one well-understood, fully mapped illicit network than a hundred cheerfully misleading ‘benign’ bots obscuring the real activity. The logs, as always, will tell the tale. And they rarely lie.
Original article: https://arxiv.org/pdf/2603.24302.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-03-26 17:54