Title: Profiling cryptocurrency influencers on social media: a comparative study using SetFit and DistilBERT

Authors: Rebeh Imane Ammar Aouchiche; Fatima Boumahdi; Mohamed Abdelkarim Remmide; Amina Guendouz

Addresses: LRDSI Laboratory, Department of Computer Science, Faculty of Sciences, University of Blida 1, Blida, Algeria ' LRDSI Laboratory, Department of Computer Science, Faculty of Sciences, University of Blida 1, Blida, Algeria ' LRDSI Laboratory, Department of Computer Science, Faculty of Sciences, University of Blida 1, Blida, Algeria ' LRDSI Laboratory, Faculty of Technology, University of Blida 1, Blida, Algeria

Abstract: Nowadays, in a world dominated by social media, the content people share can have significant effects, particularly in the domain of cryptocurrency, where investors often turn to online advice. The instability of the cryptocurrency market is well known, and some social media individuals wield considerable influence over this market through their posts. Our study focuses on categorising these influential cryptocurrency influencers based on their English tweets, with the challenge of limited data availability. Two transformer-based models: sentence transformer fine-tuning (SetFit) and distilled BERT (DistilBERT), were used to classify cryptocurrency influencers into three subtasks: profile authors based on their degree of influence, main interests, and message intent. These models were evaluated on a Twitter-based dataset from PAN2023. The results show that SetFit achieved the best performance with a 0.82 F1-score, followed closely by DistilBERT with a 0.80 F1-score.

Keywords: social media; author profiling; cryptocurrency influencers; DistilBert; SetFit; few-shot-learning.

DOI: 10.1504/IJDMMM.2026.151836

International Journal of Data Mining, Modelling and Management, 2026 Vol.18 No.1, pp.1 - 14

Received: 23 Jan 2024
Accepted: 25 Oct 2024
Published online: 23 Feb 2026 *

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