Muslihuddeen, H.Sathvika, P.Sankar, S.Ostwal, S.Anand Kumar, M.2026-02-062023CEUR Workshop Proceedings, 2023, Vol.3497, , p. 2722-273316130073https://doi.org/https://idr.nitk.ac.in/handle/123456789/29410This research provides a novel method for identifying cryptocurrency influencers on social media in a low-resource environment. The analysis focuses on English-language Twitter messages and divides influencers into impact categories ranging from minimal to massive. With a maximum of 10 English tweets per user, the dataset consists of 32 people per category. By comparing the suggested approach to two baseline models—Usercharacter Logistic Regression and t5-large (bi-encoders) using zero-shot and label tuning few-shot methods—the proposed system is evaluated using the Macro F1 measure. The findings show that the suggested approach operates effectively in low-resource environments and has the potential to be used to further in-depth studies of influencer profiling. © 2023 Copyright for this paper by its authors.cryptocurrencyfew-shotlow-resourcezero-shot