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Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never been systematically investigated before. Here, we study 9M tweets related to stocks of the five main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice—referred to as cashtag piggybacking—perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot-detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam- and bot-detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.

ACM Transactions on the Web (TWEB), 2019

Autori esterni: Fabrizio Lillo (University of Bologna), Daniele Regoli (Azimut Analytics srl)
Autori IIT:

Serena Tardelli

Foto di Serena Tardelli

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: Cresci, 2019, Cashtag Piggybacking - Uncovering Spam and Bot Activity in Stock Microblogs on Twitter.pdf

Attività: Social Media Analysis