你难道不知道，赶紧行动起来！探究电报驱动活动中的加密货币操纵行为
Don't You Know, Pump it Up! Investigating Cryptocurrency Manipulation in Telegram-Driven Activity
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中文总结 AI 辅助
本研究提出可扩展框架分析电报公开频道的加密货币操纵，识别47起潜在“拉高出货”事件，发现操纵信号同步性强且领先价格数秒，累计规模超2亿美元，发布词典与分类器供后续研究。
中文摘要 AI 辅助
电报在加密货币交流中发挥着关键作用，且多次与“拉高出货”等协同操纵计划相关联。然而，现有研究通常聚焦于已知的操纵聊天群组或有限数量的加密货币，未明确电报如何被大规模用于推广活动（即“shilling”）。为突破这些局限，本研究分析了公开电报频道中信息流与市场活动之间的相互作用。为此，我们提出了一个可扩展框架：（i）使用微调后的编码器模型对加密货币相关消息进行分类，以过滤语义噪声；（ii）通过自适应阈值检测加密货币提及量的异常峰值；（iii）采用准实验计量经济学方法（断点回归RDD和双重差分DiD）验证社交爆发与市场变动之间的时间关联。我们将该框架应用于一年的公开电报数据（涵盖14499个频道及超过2000万条消息），并匹配了超过17000种加密货币的交易数据。分析识别出47起符合潜在“拉高出货”活动的事件，以及73起持续的市场反应，表明操纵信号具有极端时间同步性，且领先价格变动数秒。值得注意的是，心理语言学分析显示，“拉高出货”消息在语言上与有机讨论无法区分，凸显了仅基于文本检测的局限性。最后，我们估算检测到的“拉高出货”事件的累计金融规模超过2亿美元，并发布了公开加密货币词典和微调后的分类器以支持未来研究。
英文摘要
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.
