Abstract

This paper explores the concept of Finfluencers, financial social network actors with high potential social influence. We investigate whether Finfluencers drive or are influenced by the broader social network sentiment and therefore clarify their role as opinion makers or opinion followers. As a dataset, we gathered a collection of 71M tweets focusing on stocks and cryptocurrencies. Based on the social networking potential (SNP) measure, we divided actors into groups of high and low potential social influence. Next, we derived sentiment time series using state-of-the-art sentiment models and applied the technique of Granger causality. Our results provide support that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger mass of actors. From the perspective of financial market regulation, this study stresses the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud.

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