Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter
This addresses the problem of fake content in financial microblogs for researchers and practitioners using such data for stock market prediction, highlighting a significant but previously unquantified issue.
The study investigated the prevalence of spam and bot activity in stock-related tweets, uncovering a practice called cashtag piggybacking where bots promote low-value stocks by exploiting high-value ones, with 71% of suspicious tweet authors classified as bots and 37% later suspended by 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 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 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.