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This study investigates the role of Telegram in cryptocurrency manipulation by analyzing over 20 million messages across 14,499 public channels, focusing on the relationship between information flows and market activity. A novel scalable framework was developed to classify crypto-related messages, detect anomalous spikes in mentions, and validate the timing of social bursts against market movements using econometric methods. The findings reveal 47 potential pump-and-dump events and 73 sustained market reactions, with manipulative signals often preceding price changes by mere seconds, while also highlighting the challenges of distinguishing manipulative messages from organic discussions.
Manipulative signals in cryptocurrency markets can precede price movements by just seconds, revealing the urgent need for more sophisticated detection methods.
Telegram plays a pivotal role in cryptocurrency communication and has been repeatedly associated with coordinated schemes, such as pump-and-dump manipulation. However, existing studies typically focus on known manipulation chats or a limited set of cryptocurrencies, leaving open the question of how Telegram is leveraged for mass promotional activity (shilling) at scale. Moving beyond these limitations, this work analyzes the interplay between information flows and market activity across public Telegram channels. To this end, we propose a scalable framework that (i) classifies crypto-related messages using a fine-tuned encoder model to filter semantic noise, (ii) detects anomalous spikes in cryptocurrency mentions via adaptive thresholding, and (iii) validates temporal associations between social bursts and market movements using quasi-experimental econometric methods (RDD and DiD). We apply this framework to one year of public Telegram data (14,499 channels and over 20 million messages) aligned with transaction data for more than 17,000 cryptocurrencies. Our analysis identifies 47 events consistent with potential pump-and-dump activity and 73 sustained market reactions, showing that manipulative signals are characterized by extreme temporal synchronization and precede price movements by seconds. Notably, psycholinguistic analysis reveals that pump-and-dump messages are linguistically indistinguishable from organic discussions, highlighting the limits of text-based detection alone. Finally, we estimate the cumulative financial volume of detected pump-and-dump events to exceed $200 million and release a public cryptocurrency dictionary and a fine-tuned classifier to support future research.