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Cited by 105Spam and Phishing DetectionMisinformation and Its ImpactsStock Market Forecasting Methods

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

Cresci, Stefano, Lillo, Fabrizio, Regoli, Daniele, Tardelli, Serena, Tesconi, Maurizio·2019·ACM Transactions on the Web
DOI 10.1145/3313184
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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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  1. Luca Maria Aiello , Martina Deplano , Rossano Schifanella , and Giancarlo Ruffo . 2012 . People are strange when you’re a stranger: Impact and influence of bots on social networks . In Proceedings of the 6th International Conference on Web and Social Media (ICWSM’12) . AAAI. Luca Maria Aiello, Martina Deplano, Rossano Schifanella, and Giancarlo Ruffo. 2012. People are strange when you’re a stranger: Impact and influence of bots on social networks. In Proceedings of the 6th International Conference on Web and Social Media (ICWSM’12). AAAI.
  2. 10.5555/3191835.3191912 · DOI
  3. Marco T. Bastos and Dan Mercea . 2017 . The Brexit botnet and user-generated hyperpartisan news. Soc. Sci. Comput. Rev . (2017), 0894439317734157. Marco T. Bastos and Dan Mercea. 2017. The Brexit botnet and user-generated hyperpartisan news. Soc. Sci. Comput. Rev. (2017), 0894439317734157.
  4. Johan Bollen , Huina Mao , and Alberto Pepe . 2011 . Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena . In Proceedings of the 5th International Conference on Web and Social Media (ICWSM’11) . AAAI, 450--453. Johan Bollen, Huina Mao, and Alberto Pepe. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the 5th International Conference on Web and Social Media (ICWSM’11). AAAI, 450--453.
  5. Florian Brachten , Stefan Stieglitz , Lennart Hofeditz , Katharina Kloppenborg , and Annette Reimann . 2017 . Strategies and influence of social bots in a 2017 German state election—A case study on Twitter . In Proceedings of the 28th Australasian Conference on Information Systems (ACIS’17) . Florian Brachten, Stefan Stieglitz, Lennart Hofeditz, Katharina Kloppenborg, and Annette Reimann. 2017. Strategies and influence of social bots in a 2017 German state election—A case study on Twitter. In Proceedings of the 28th Australasian Conference on Information Systems (ACIS’17).
  6. Stefano Cresci , Fabrizio Lillo , Daniele Regoli , Serena Tardelli , and Maurizio Tesconi . 2018 . $FAKE: Evidence of spam and bot activity in stock microblogs on Twitter . In Proceedings of the 12th International Conference on Web and Social Media (ICWSM’18) . AAAI, 580--583. Stefano Cresci, Fabrizio Lillo, Daniele Regoli, Serena Tardelli, and Maurizio Tesconi. 2018. $FAKE: Evidence of spam and bot activity in stock microblogs on Twitter. In Proceedings of the 12th International Conference on Web and Social Media (ICWSM’18). AAAI, 580--583.
  7. Martin Hentschel and Omar Alonso . 2014. Follow the money: A study of cashtags on Twitter. First Mon. 19, 8 ( 2014 ). Martin Hentschel and Omar Alonso. 2014. Follow the money: A study of cashtags on Twitter. First Mon. 19, 8 (2014).
  8. Andrew Tanenbaum and David Wetherall . 2014. Computer Networks . 5 th Edition. Pearson Education Limited . Andrew Tanenbaum and David Wetherall. 2014. Computer Networks. 5th Edition. Pearson Education Limited.
  9. Onur Varol , Emilio Ferrara , Clayton A. Davis , Filippo Menczer , and Alessandro Flammini . 2017 . Online human-bot interactions: Detection, estimation, and characterization . In Proceedings of the 11th International Conference on Web and Social Media (ICWSM’17) . AAAI. Onur Varol, Emilio Ferrara, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online human-bot interactions: Detection, estimation, and characterization. In Proceedings of the 11th International Conference on Web and Social Media (ICWSM’17). AAAI.
  10. Ilya Zheludev , Robert Smith , and Tomaso Aste . 2014 . When can social media lead financial markets? Sci . Rep. 4 (2014), 4213 . Ilya Zheludev, Robert Smith, and Tomaso Aste. 2014. When can social media lead financial markets? Sci. Rep. 4 (2014), 4213.

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Metadata

Authors
Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M.
Affiliations
  • Institute of Informatics and Telematics (IT)
  • Scuola Normale Superiore (IT)
ORCID
Venue
ACM Transactions on the Web
Publisher
Association for Computing Machinery (ACM)
Published
2019-04-03
Volume
13
Issue
2
Pages
1-27
ISSN
  • 1559-1131 (ISSN-L)
  • 1559-114X
DOI
10.1145/3313184
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
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
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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