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Telegram has emerged as a major platform for large-scale video piracy, where copyrighted content is rapidly distributed among users. Despite its prominence, the structural and operational dynamics of this ecosystem remain insufficiently understood. To address this gap, we present the first large-scale study of video piracy on Telegram through a mixed-method analysis of 1,057 channels that shared 209k unique posts between December 2023 and January 2026 - systematically characterizing their content, distribution strategies, and how the ecosystem is sustained at scale. Central to our approach is the development of a fine-grained taxonomy that enables a structured understanding of the activity and intent of these channels on a per-post level. The channels collectively distributed 19,033 unique copyrighted titles originating from 175 countries, accumulating over 4.85B unique views and resulting in a lower-bound estimated financial loss of $17.49B for content rights holders. We also find that this ecosystem is deliberately engineered to be resilient against takedown efforts, frequently redirecting users through chains of intermediary channels and automated bots that collectively handle h

Telegram, initially a messaging app, has evolved into a platform where users can interact with various services through programmable applications, bots. Bots provide a wide range of uses, from moderating groups, helping with online shopping, to even executing trades in financial markets. However, Telegram has been increasingly associated with various illicit activities -- financial scams, stolen data, non-consensual image sharing, among others, raising concerns bots may be facilitating these operations. This paper is the first to characterize Telegram bots at scale, through the following contributions. First, we offer the largest general-purpose message dataset and the first bot dataset. Through snowball sampling from two published datasets, we uncover over 67,000 additional channels, 492 million messages, and 32,000 bots. Second, we develop a system to automatically interact with bots in order to extract their functionality. Third, based on their description, chat responses, and the associated channels, we classify bots into several domains. Fourth, we investigate the communities each bot serves, by analyzing supported languages, usage patterns (e.g., duration, reuse), and network

This paper presents TeleHunt, a framework and tool for evaluating the effectiveness of different strategies to discover cybercriminal communities on Telegram. TeleHunt employs a set of reference-driven snowballing strategies, integrating message-level classification, contextual filtering, and market-segment labeling. Using open- and dark-web seeds, we systematically evaluate how seed source, pointer type, and exploration strategy influence discovery outcomes in three dimensions: efficiency, accessibility, and rediscovery. Our work provides (i) a modular cybercrime content discovery pipeline, (ii) the first systematic comparison of Telegram discovery strategies with an empirical characterization of market-segment accessibility, and (iii) a labeled dataset of over 172 million messages from 6,022 Telegram communities.

Cryptocurrency pump-and-dump schemes coordinated via Telegram threaten market integrity. However, existing research addressing this specific threat has not yet produced solutions that combine reliable results with fast response. This is in part due to the absence of publicly available, message-level labeled data, as well as design choices. In this paper, we address both issues. In particular, we introduce a corpus of over 280,000 Telegram posts from 39 pump-organizing groups, all manually reviewed to identify 2,246 pump announcements and their targeted cryptocurrency and exchange. Leveraging this dataset, we define two tasks: real-time pump-announcement detection and target cryptocurrency/exchange extraction. For detection, we compare two machine-learning models: a lightweight tree-based LightGBM classifier (F1=0.79, latency=9.4 s/sample) and a transformer-based BGE-M3 (F1=0.83, latency=50 ms/sample). With our proposed approach, we show that message analysis can achieve near-instant pump detection at the level of individual Telegram message windows. Unlike prior work that relies purely on market data and typically detects pumps tens of seconds after abnormal trading activity is obs

Telegram is increasingly used for political communication and news dissemination, yet evidence of coordinated content sharing remains limited. We test whether mainstream global news channels coordinate when reporting on Venezuela during political turbulence. We analyze public Telegram posts from nine major international outlets (2017--2026; 2,038 Venezuela-related messages) and define coordination as temporal co-posting (hourly/daily windows) plus near-duplicate text similarity using character $n$-gram TF--IDF cosine similarity. Similarity scores concentrate at low values and no cross-channel near-duplicate pairs are detected at $τ=0.85$. A falsification test that randomizes timestamps within channels produces the same null result, indicating the pipeline does not create spurious coordination. Event-focused diagnostics show temporal lead--lag asymmetries consistent with heterogeneous editorial responsiveness, and narrative clustering during the January 3--6, 2026 peak reveals moderate framing diversity without separable narrative blocs. An Attention--Coordination Ratio formalizes sharp attention spikes in early January 2026 despite absent near-duplicate coordination, distinguishing

Here we present a massive longitudinal dataset of public Telegram content, comprising over 5.9 billion messages dating from 2015 to 2025, collected from 712 thousand channels and groups, enriched with metadata on forwards, reactions, and polls. The dataset spans multiple languages including Russian and Farsi, representing countries where Telegram shows mainstream adoption, as well as Western languages where Telegram is used in specific sub-communities. The dataset has several advantages. First, when restricted by language, it provides a versatile example of an algorithm-free platform, contrary to many other social media platforms that are strongly influenced by opaque content-curation algorithms. Second, it enables comparative studies across different languages, communities, and user bases under identical platform affordances. The dataset thus offers a foundation for studying engagement patterns, network evolution, and community formation in the absence of algorithmic curation.

Conspiracy theories commonly attribute important events to the actions of powerful and secretive actors. While computational research has largely focused on document-level analyses of conspiracy theories, less attention has been paid to identifying the actors that drive such narratives. We develop annotation guidelines for conspiratorial actors, present a span-annotated corpus of German Telegram posts, and investigate their automatic extraction using transformer-based models. We further apply the resulting model to the \textit{Schwurbelarchiv}, a large-scale archive of German conspiracy-related Telegram channels. Our results demonstrate that conspiratorial actors can be annotated with meaningful agreement and extracted with reasonable accuracy despite the linguistic complexity of conspiracy discourse, enabling large-scale analyses of actor representations in conspiracy narratives.

Over the past decade, Brazil has experienced a decline in vaccination coverage, reversing decades of public health progress achieved through the National Immunization Program (PNI). Growing evidence points to the widespread circulation of vaccine-related misinformation -- particularly on social media platforms -- as a key factor driving this decline. Among these platforms, Telegram remains the only major platform permitting accessible and ethical data collection, offering insight into public channels where vaccine misinformation circulates extensively. This data paper introduces a curated dataset of about four million Telegram posts collected from 119 prominent Brazilian anti-vaccine channels between 2020 and 2025. The dataset includes message content, metadata, associated media, and classification related to vaccine posts, enabling researchers to examine how false or misleading information spreads, evolves, and influences public sentiment. By providing this resource, our aim is to support the scientific and public health community in developing evidence-based strategies to counter misinformation, promote trust in vaccination, and engage compassionately with individuals and communi

This study examines how Russian far-right communities on Telegram shape perceptions of political figures through memes and visual narratives. Far from passive spectators, these actors co-produce propaganda, blending state-aligned messages with their own extremist framings. In Russia, such groups are central because they articulate the ideological foundations of the war against Ukraine and reflect the regime's gradual drift toward ultranationalist rhetoric. Drawing on a dataset of 200,000 images from expert-selected far-right Telegram channels, the study employs computer vision and unsupervised clustering to identify memes featuring Russian (Putin, Shoigu) and foreign politicians (Zelensky, Biden, Trump) and to reveal recurrent visual patterns in their representation. By leveraging the large-scale and temporal depth of this dataset, the analysis uncovers differential patterns of legitimation and delegitimation across actors and over time. These insights are not attainable in smaller-scale studies. Preliminary findings show that far-right memes function as instruments of propaganda co-production. These communities do not simply echo official messages but generate bottom-up narratives

Telegram has become one of the leading platforms for disseminating misinformational messages. However, many existing pipelines still classify each message's credibility based on the reputation of its associated domain names or its lexical features. Such methods work well on traditional long-form news articles published by well-known sources, but high-risk posts on Telegram are short and URL-sparse, leading to failures for link-based and standard TF-IDF models. To this end, we propose the TAG2CRED pipeline, a method designed for such short, convoluted messages. Our model will directly score each post based on the tags assigned to the text. We designed a concise label system that covers the dimensions of theme, claim type, call to action, and evidence. The fine-tuned large language model (LLM) assigns tags to messages and then maps these tags to calibrated risk scores in the [0,1] interval through L2-regularized logistic regression. We evaluated 87,936 Telegram messages associated with Media Bias/Fact Check (MBFC), using URL masking and domain disjoint splits. The results showed that the ROC-AUC of the TAG2CRED model reached 0.871, the macro-F1 value was 0.787, and the Brier score wa

Telegram emerged as a crucial platform for both parties during the conflict between Russia and Ukraine. Per its minimal policies for content moderation, Pro-Kremlin narratives and potential misinformation were spread on Telegram, while anti-Kremlin narratives with related content were also propagated, such as war footage, troop movements, maps of bomb shelters, and air raid warnings. This paper presents a dataset of posts from both pro-Kremlin and anti-Kremlin Telegram channels, collected over a period spanning a year before and a year after the Russian invasion. The dataset comprises 404 pro-Kremlin channels with 4,109,645 posts and 114 anti-Kremlin channels with 1,117,768 posts. We provide details on the data collection process, processing methods, and dataset characterization. Lastly, we discuss the potential research opportunities this dataset may enable researchers across various disciplines.

Connectivity and message propagation are central, yet often underutilized, sources of information in misinformation detection -- especially on poorly moderated platforms such as Telegram, which has become a critical channel for misinformation dissemination, namely in the German electoral context. In this paper, we introduce Misinfo-TeleGraph, the first German-language Telegram-based graph dataset for misinformation detection. It includes over 5 million messages from public channels, enriched with metadata, channel relationships, and both weak and strong labels. These labels are derived via semantic similarity to fact-checks and news articles using M3-embeddings, as well as manual annotation. To establish reproducible baselines, we evaluate both text-only models and graph neural networks (GNNs) that incorporate message forwarding as a network structure. Our results show that GraphSAGE with LSTM aggregation significantly outperforms text-only baselines in terms of Matthews Correlation Coefficient (MCC) and F1-score. We further evaluate the impact of subscribers, view counts, and automatically versus human-created labels on performance, and highlight both the potential and challenges

Telegram has become a major space for political discourse and alternative media. However, its lack of moderation allows misinformation, extremism, and toxicity to spread. While prior research focused on these particular phenomena or topics, these have mostly been examined separately, and a broader understanding of the Telegram ecosystem is still missing. In this work, we fill this gap by conducting a large-scale analysis of the Italian Telegram sphere, leveraging a dataset of 186 million messages from 13,151 chats collected in 2023. Using network analysis, Large Language Models, and toxicity detection tools, we examine how different thematic communities form, align ideologically, and engage in harmful discourse within the Italian cultural context. Results show strong thematic and ideological homophily. We also identify mixed ideological communities where far-left and far-right rhetoric coexist on particular geopolitical issues. Beyond political analysis, we find that toxicity, rather than being isolated in a few extreme chats, appears widely normalized within highly toxic communities. Moreover, we find that Italian discourse primarily targets Black people, Jews, and gay individuals

Cyber Threat Intelligence (CTI) enables organizations to anticipate, detect, and mitigate evolving cyber threats. Its effectiveness depends on high-quality datasets, which support model development, training, evaluation, and benchmarking. Building such datasets is crucial, as attack vectors and adversary tactics continually evolve. Recently, Telegram has gained prominence as a valuable CTI source, offering timely and diverse threat-related information that can help address these challenges. In this work, we address these challenges by presenting an end-to-end automated pipeline that systematically collects and filters threat-related content from Telegram. The pipeline identifies relevant Telegram channels and scrapes 145,349 messages from 12 curated channels out of 150 identified sources. To accurately filter threat intelligence messages from generic content, we employ a BERT-based classifier, achieving an accuracy of 96.64%. From the filtered messages, we compile a dataset of 86,509 malicious Indicators of Compromise, including domains, IPs, URLs, hashes, and CVEs. This approach not only produces a large-scale, high-fidelity CTI dataset but also establishes a foundation for future

The paper proposes an advanced approach for identifying disinformation on Telegram channels related to the Russo-Ukrainian conflict, utilizing state-of-the-art (SOTA) deep learning techniques and transfer learning. Traditional methods of disinformation detection, often relying on manual verification or rule-based systems, are increasingly inadequate in the face of rapidly evolving propaganda tactics and the massive volume of data generated daily. To address these challenges, the proposed system employs deep learning algorithms, including LLM models, which are fine-tuned on a custom dataset encompassing verified disinformation and legitimate content. The paper's findings indicate that this approach significantly outperforms traditional machine learning techniques, offering enhanced contextual understanding and adaptability to emerging disinformation strategies.

This study examines elite-driven political communication on Telegram during the ongoing Russo-Ukrainian war, the first large-scale European war in the social media era. Using a unique dataset of Telegram public posts from Ukrainian and Russian policymakers (2019-2024), we analyze changes in communication volume, thematic content, and actor engagement following Russia's 2022 full-scale invasion. Our findings show a sharp increase in Telegram activity after the invasion, particularly among ruling-party policymakers. Ukrainian policymakers initially focused on war-related topics, but this emphasis declined over time In contrast, Russian policymakers largely avoided war-related discussions, instead emphasizing unrelated topics, such as Western crises, to distract public attention. We also identify differences in communication strategies between large and small parties, as well as individual policymakers. Our findings shed light on how policymakers adapt to wartime communication challenges and offer critical insights into the dynamics of online political discourse during times of war.

Telegram is a popular platform for public communication, generating large amounts of messages through its channels. pytopicgram is a Python library that helps researchers collect, organize, and analyze these Telegram messages. The library offers key features such as easy message retrieval, detailed channel information, engagement metrics, and topic identification using advanced modeling techniques. By simplifying data extraction and analysis, pytopicgram allows users to understand how content spreads and how audiences interact on Telegram. This paper describes the design, main features, and practical uses of \pytopicgram, showcasing its effectiveness for studying public conversations on Telegram.

Sociality borne by language, as is the predominant digital trace on text-based social media platforms, harbours the raw material for exploring a multitude of social phenomena. Distinctively, the messaging service Telegram provides functionalities that allow for socially interactive as well as one-to-many communication. Our Telegram dataset contains over 5,800 groups and channels and 63 million messages, originating from a data-hoarding initiative named the ``Schwurbelarchiv'' (from German schwurbeln: speaking nonsense). Uniquely, it includes the transcriptions of over 3 million audio and video files. While the raw data was previously archived on the Internet Archive by an anonymous data hoarder, it was stored in a format that is difficult to process and largely inaccessible for systematic research. Our contribution consists of parsing, cleaning, and validating this raw archive, pseudonymising user data, and transcribing roughly 126,000 hours of audio and video content, thereby transforming this data hoard into a structured, research-ready dataset. This dataset publication details the structure, scope, and methodological specifics of the Schwurbelarchiv, emphasising its relevance fo

This project addresses a critical pedagogical need: offering students continuous, on-demand academic assistance beyond conventional reception hours. I present a domain-specific Retrieval-Augmented Generation (RAG) system powered by a quantized Mistral-7B Instruct model and deployed as a Telegram bot. The assistant enhances learning by delivering real-time, personalized responses aligned with the "Introduction to Parallel Processing" course materials. GPU acceleration significantly improves inference latency, enabling practical deployment on consumer hardware. This approach demonstrates how consumer GPUs can enable affordable, private, and effective AI tutoring for HPC education.