PumpSense: Real-Time Detection and Target Extraction of Crypto Pump-and-Dumps on Telegram
Ahmed Mahrous, Roberto Di Pietro

TL;DR
This paper presents PumpSense, a real-time Telegram message analysis system for detecting and extracting crypto pump-and-dump schemes, achieving high accuracy and low latency with a new labeled dataset.
Contribution
It introduces a large, manually labeled Telegram dataset and compares machine learning models for instant pump detection and target extraction, establishing new benchmarks.
Findings
LightGBM achieves F1=0.79 with 9.4 s latency.
BGE-M3 transformer achieves F1=0.83 with 50 ms latency.
LLMs outperform rule-based methods with 0.91 accuracy.
Abstract
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,…
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