Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Predicting Artificial Inflation in Cryptocurrency Prices Using a Hybrid Approach

Document Type : Persian Original Article

Authors
Faezeh Mousavi-Firouzkola
Hamid Moradi-Kamali
Reza Entezari-Maleki
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
10.22034/jscit.2026.559514.2195
Abstract
Pump-and-dump (P&D) schemes, orchestrated through social media, are a prevalent form of manipulation in cryptocurrency markets. While most research has focused on detecting these events, the critical challenge of predicting the specific target coin remains largely underexplored. Existing attempts at target prediction often analyze textual content or time-series anomalies for individual coins in isolation. This paper introduces a fundamentally different approach, framing target prediction as a sequential problem where the history of a P&D channel's activity informs the next target. In this paper, we firstly develop a robust labeling method that integrates textual signals from Telegram with financial data, identifying 1,200 P&D events from 2022-2024. In the second step, we design a sequence-based predictive model that uses self-attention to learn historical P&D patterns within a channel and cross-attention to correlate these patterns with the features of potential target coins. Evaluated on real-world data, our proposed model achieves an Area Under the Curve (AUC) of 89% and demonstrates a significant improvement of approximately 10% on the Hit Ratio at K (HR@K) metric compared to a state-of-the-art baseline. Our findings demonstrate that modeling the sequential context of manipulation campaigns is a superior strategy for proactively identifying target coins. To access the code and data related to the approach presented in this paper, you may refer to link "https://github.com/faezemousavi/Pump-and-Dump".
Keywords
Cryptocurrency Market
Market Manipulation
Fraud Detection
Text Analysis
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Journal of Soft Computing and Information Technology
Volume 15, Issue 2
Summer - August
Summer 2026
Pages 64-77