Cryptocurrency Topic Burst Prediction via Hybrid Twin-structured Multi-modal Learning

Keting Yin, Xiaen Sun, Tian Feng

Abstract: Social media users actively engage in discussions concerning news and events within the dynamic cryptocurrency market, resulting in the widespread dissemination of cryptocurrency-related topics across various platforms. The ongoing monitoring of these topics is crucial to informed investment, effective platform regulation, and heightened community engagement. Nevertheless, the abruptness of bursts in cryptocurrency-related topics presents significant challenges to conventional prediction methods based on features and time series, particularly in understanding their regularity and causation. To address these challenges, this paper introduces Nostredame, an innovative cryptocurrency topic burst prediction method based on hybrid twin-structured multi-modal learning. By employing a defined burst score as a quantitative measure for day-wise topic evolution, the proposed method aims to predict bursts from a multi-modal perspective. This is achieved through hyperbolic temporal encoders that capture temporal behaviors with cross-modal representations in the twin structure, followed by the fusion within the hybrid learning module. Experimental results indicate that cryptocurrency transactions, social media contents, and topic evolution collectively influence topic bursts, and the proposed method notably outperforms all baseline methods across common metrics, highlighting its efficacy in predicting cryptocurrency-related topic bursts.