Paper Info
Title
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter.
Abstract
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.
Year
DOI
Venue
2018
10.1145/3313184
ACM Transactions on the Web (TWEB)
Keywords
Field
DocType
Social spam, Twitter, social networks security, spam and bot detection, stock market
Data science,Piggybacking (Internet access),Computer science,Popularity,Spambot,Artificial intelligence,Financial market,Stock market,Social media,Microblogging,Exploit,Stock (geology),Machine learning
Journal
Volume
Issue
ISSN
abs/1804.04406
2
ACM Transactions on the Web 13(2), 2019
Citations 
PageRank 
References 
4
0.46
62
Authors
5
Name
Order
Citations
PageRank
Cresci, S.123521.79
Fabrizio Lillo24110.66
Daniele Regoli340.46
Serena Tardelli471.86
Maurizio Tesconi528132.06