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Characterizing Social Bots Spreading Financial Disinformation

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Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis (HCII 2020)

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Abstract

Despite the existence of several studies on the characteristics and role of social bots in spreading disinformation related to politics, health, science and education, financial social bots remain a largely unexplored topic. We aim to shed light on this issue by investigating the activities of large social botnets in Twitter, involved in discussions about stocks traded in the main US financial markets. We show that the largest discussion spikes are in fact caused by mass-retweeting bots. Then, we focus on characterizing the activity of these financial bots, finding that they are involved in speculative campaigns aimed at promoting low-value stocks by exploiting the popularity of high-value ones. We conclude by highlighting the peculiar features of these accounts, comprising similar account creation dates, similar screen names, biographies, and profile pictures. These accounts appear as untrustworthy and quite simplistic bots, likely aiming to fool automatic trading algorithms rather than human investors. Our findings pave the way for the development of accurate detection and filtering techniques for financial spam. In order to foster research and experimentation on this novel topic, we make our dataset publicly available for research purposes.

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Notes

  1. 1.

    https://www.bbc.com/news/world-us-canada-21508660.

  2. 2.

    https://www.cnbc.com/2014/07/25/mysterious-stock-cynk-plummets-after-reopening.html.

  3. 3.

    http://www.nasdaq.com/screening/company-list.aspx.

  4. 4.

    https://www.google.com/finance.

  5. 5.

    https://doi.org/10.5281/zenodo.2686862.

  6. 6.

    TRBC is a 5-level hierarchical sector and industry classification, widely used in the financial domain for computing sector-specific indices: https://en.wikipedia.org/wiki/Thomson_Reuters_Business_Classification.

  7. 7.

    https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html.

References

  1. Albadi, N., Kurdi, M., Mishra, S.: Hateful people or hateful bots? Detection and characterization of bots spreading religious hatred in Arabic social media. In: Proceedings of the ACM on Human-Computer Interaction (HCI), vol. 3, no. CSCW, pp. 1–25 (2019)

    Google Scholar 

  2. Allem, J.P., Ferrara, E.: Could social bots pose a threat to public health? Am. J. Public Health (AJPH) 108(8), 1005 (2018)

    Article  Google Scholar 

  3. Berger, J.M., Morgan, J.: The ISIS Twitter census: defining and describing the population of ISIS supporters on Twitter. The Brookings Project on US Relations with the Islamic World, vol. 3, no. 20, pp. 1–4 (2015)

    Google Scholar 

  4. Beskow, D.M., Carley, K.M.: Its all in a name: detecting and labeling bots by their name. Comput. Math. Organ. Theory (CMOT) 25(1), 24–35 (2019). https://doi.org/10.1007/s10588-018-09290-1

    Article  Google Scholar 

  5. Bessi, A., Ferrara, E.: Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21 (2016). https://doi.org/10.5210/fm.v21i11.7090

  6. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. (JCS) 2(1), 1–8 (2011)

    Article  Google Scholar 

  7. Brachten, F., Stieglitz, S., Hofeditz, L., Kloppenborg, K., Reimann, A.: Strategies and influence of social bots in a 2017 German state election - a case study on Twitter. In: The 28th Australasian Conference on Information Systems (ACIS 2017) (2017)

    Google Scholar 

  8. Cresci, S.: Detecting malicious social bots: story of a never-ending clash. In: Grimme, C., Preuss, M., Takes, F.W., Waldherr, A. (eds.) MISDOOM 2019. LNCS, vol. 12021, pp. 77–88. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39627-5_7

    Chapter  Google Scholar 

  9. Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: Fame for sale: efficient detection of fake Twitter followers. Decis. Support Syst. (DSS) 80, 56–71 (2015)

    Article  Google Scholar 

  10. Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling. IEEE Trans. Dependable Secure Comput. (TDSC) 15(4), 561–576 (2017)

    Google Scholar 

  11. Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: evidence, theories, and tools for the arms race. In: The 26th International Conference on World Wide Web Companion (WWW 2017 Companion), pp. 963–972 (2017). IW3C2

    Google Scholar 

  12. Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: Emergent properties, models and laws of behavioral similarities within groups of Twitter users. Comput. Commun. 150, 47–61 (2020)

    Article  Google Scholar 

  13. Cresci, S., Lillo, F., Regoli, D., Tardelli, S., Tesconi, M.: \$FAKE: evidence of spam and bot activity in stock microblogs on Twitter. In: The 12th International AAAI Conference on Web and Social Media (ICWSM 2018). AAAI (2018)

    Google Scholar 

  14. Cresci, S., Lillo, F., Regoli, D., Tardelli, S., Tesconi, M.: Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter. ACM Trans. Web (TWEB) 13(2), 11:1–11:27 (2019)

    Google Scholar 

  15. Cresci, S., Minutoli, S., Nizzoli, L., Tardelli, S., Tesconi, M.: Enriching digital libraries with crowdsensed data. In: Manghi, P., Candela, L., Silvello, G. (eds.) IRCDL 2019. CCIS, vol. 988, pp. 144–158. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11226-4_12

    Chapter  Google Scholar 

  16. Ferrara, E.: Disinformation and social bot operations in the run up to the 2017 French Presidential election. First Monday 22(8) (2017). https://doi.org/10.5210/fm.v22i8.8005

  17. Giatsoglou, M., Chatzakou, D., Shah, N., Faloutsos, C., Vakali, A.: Retweeting activity on Twitter: signs of deception. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9077, pp. 122–134. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18038-0_10

    Chapter  Google Scholar 

  18. Hentschel, M., Alonso, O.: Follow the money: a study of cashtags on Twitter. First Monday 19(8) (2014). https://doi.org/10.5210/fm.v19i8.5385

  19. Hwang, T., Pearce, I., Nanis, M.: Socialbots: voices from the fronts. Interactions 19(2), 38–45 (2012)

    Article  Google Scholar 

  20. Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: CatchSync: catching synchronized behavior in large directed graphs. In: The 20th SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD 2014), pp. 941–950. ACM (2014)

    Google Scholar 

  21. Lee, S., Kim, J.: Early filtering of ephemeral malicious accounts on Twitter. Comput. Commun. 54, 48–57 (2014)

    Article  Google Scholar 

  22. Mao, Y., Wei, W., Wang, B., Liu, B.: Correlating S&P 500 stocks with Twitter data. In: The 1st International Workshop on Hot Topics on Interdisciplinary Social Networks Research (SIGKDD 2012 Workshops), pp. 69–72. ACM (2012)

    Google Scholar 

  23. Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., Tesconi, M.: RTbust: exploiting temporal patterns for botnet detection on Twitter. In: The 11th International ACM Web Science Conference (WebSci 2019), pp. 183–192. ACM (2019)

    Google Scholar 

  24. Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M., Ferrara, E.: Charting the landscape of online cryptocurrency manipulation. arXiv preprint arXiv:2001.10289 (2020)

  25. Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nat. Commun. 9(1), 4787 (2018)

    Article  Google Scholar 

  26. Stella, M., Ferrara, E., De Domenico, M.: Bots increase exposure to negative and inflammatory content in online social systems. Proc. Natl. Acad. Sci. 115(49), 12435–12440 (2018)

    Article  Google Scholar 

  27. Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: The 11th International AAAI Conference on Web and Social Media (ICWSM 2017). AAAI (2017)

    Google Scholar 

  28. Varol, O., Ferrara, E., Menczer, F., Flammini, A.: Early detection of promoted campaigns on social media. EPJ Data Sci. 6(1), 1–19 (2017). https://doi.org/10.1140/epjds/s13688-017-0111-y

    Article  Google Scholar 

  29. Viswanath, B., et al.: Strength in numbers: robust tamper detection in crowd computations. In: The 2015 ACM Conference on Online Social Networks (COSN 2015), pp. 113–124. ACM (2015)

    Google Scholar 

  30. Zannettou, S., Caulfield, T., De Cristofaro, E., Sirivianos, M., Stringhini, G., Blackburn, J.: Disinformation warfare: understanding state-sponsored trolls on Twitter and their influence on the Web. In: The 2019 World Wide Web Conference Companion (WWW 2019 Companion), pp. 218–226 (2019). IW3C2

    Google Scholar 

  31. Zannettou, S., Caulfield, T., Setzer, W., Sirivianos, M., Stringhini, G., Blackburn, J.: Who let the trolls out? Towards understanding state-sponsored trolls. In: The 11th International ACM Web Science Conference (WebSci 2019), pp. 353–362. ACM (2019)

    Google Scholar 

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Acknowledgements

This work is partially supported by the European Community’s H2020 Program under the scheme INFRAIA-1-2014-2015: Research Infrastructures, grant agreement #654024 SoBigData: Social Mining and Big Data Ecosystem and the scheme INFRAIA-01-2018-2019: Research and Innovation action, grant agreement #871042 SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics, and by the Italian Ministry of Education and Research in the framework of the CrossLab Project (Cloud Computing, Big Data & Cybersecurity), Departments of Excellence.

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Authors and Affiliations

  1. IIT-CNR and Department of Information Engineering, University of Pisa, Pisa, Italy

    Serena Tardelli

  2. Department of Information Engineering, University of Pisa, Pisa, Italy

    Marco Avvenuti

  3. IIT-CNR, Pisa, Italy

    Maurizio Tesconi & Stefano Cresci

Authors
  1. Serena Tardelli
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  2. Marco Avvenuti
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  3. Maurizio Tesconi
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  4. Stefano Cresci
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Corresponding author

Correspondence to Stefano Cresci .

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Editors and Affiliations

  1. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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Tardelli, S., Avvenuti, M., Tesconi, M., Cresci, S. (2020). Characterizing Social Bots Spreading Financial Disinformation. In: Meiselwitz, G. (eds) Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. HCII 2020. Lecture Notes in Computer Science(), vol 12194. Springer, Cham. https://doi.org/10.1007/978-3-030-49570-1_26

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  • DOI: https://doi.org/10.1007/978-3-030-49570-1_26

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