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Microblogs are increasingly exploited for predicting prices and tradedvolumes of stocks in financial markets. However, it has been demonstrated thatmuch of the content shared in microblogging platforms is created and publicizedby bots and spammers. Yet, the presence (or lack thereof) and the impact offake stock microblogs has never systematically been investigated before. Here,we study 9M tweets related to stocks of the 5 main financial markets in the US.By comparing tweets with financial data from Google Finance, we highlightimportant characteristics of Twitter stock microblogs. More importantly, weuncover a malicious practice - referred to as cashtag piggybacking -perpetrated by coordinated groups of bots and likely aimed at promotinglow-value stocks by exploiting the popularity of high-value ones. Among thefindings of our study is that as much as 71% of the authors of suspiciousfinancial tweets are classified as bots by a state-of-the-art spambot detectionalgorithm. Furthermore, 37% of them were suspended by Twitter a few monthsafter our investigation. Our results call for the adoption of spam and botdetection techniques in all studies and applications that exploituser-generated content for predicting the stock market.

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter