The DOI matches its Crossref record; 77 of the 78 references it cites were found in a registry or an index.
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.
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“A 2019 study reported that 71% of Twitter users mentioning trending US stocks, are likely to be bots [8].”
From A decade of social bot detection (2020) · cites this paper as [8]
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Self-citation
Metadata
- Authors
- Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M.
- Affiliations
- Institute of Informatics and Telematics (IT)
- Scuola Normale Superiore (IT)
- Venue
- ACM Transactions on the Web
- Published
- 2019-04-03
- Volume
- 13
- Issue
- 2
- Pages
- 1-27
- ISSN
- 1559-1131 (ISSN-L)
- 1559-114X
- OpenAlex
- W4288373381
- Semantic Scholar
- f9cdba7f939f3f33711c5f25c0ab9b31350549f7
- Cited by
- 105
- References
- 78
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 18 · 2023: 18 · 2022: 15 · 2021: 14 · 2020: 15 · 2019: 4
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on the Web (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Funders
- Horizon 2020 Framework Programme (654024)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- Twitter, bot detection, fake accounts, stock market prediction, user-generated content
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