2018/04/30 by Stefano Cresci, Fabrizio Lillo, Daniele Regoli +2
Computer Science · Engineering · Social Sciences · #Blockchain Technology Applications and Security #Business #Computer science #Computer security #Engineering #Exploit #Finance #Financial market #Geography #Internet privacy #Microblogging #Misinformation and Its Impacts #Popularity #Social media #Spam and Phishing Detection #Spamming #Stock (firearms) #Stock market #The Internet #World Wide Web #cs.CR #cs.SI
paper · pdf · doi:10.1145/3313184
published as ACM Transactions on the Web 13(2), 2019
arxiv created 2018/07/17 · openalex publication_date 2019/04/03 · arxiv updated 2020/06/29 · openalex created_date 2022/07/29 · openalex updated_date 2026/06/11
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.