Skip to main content
SpringerLink
Log in

Twitter Bots Influence on the Russo-Ukrainian War During the 2022 Italian General Elections

  • Conference paper
  • First Online:
Security and Privacy in Social Networks and Big Data (SocialSec 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14097))

Abstract

In February 2022, Russia launched a full-scale invasion of Ukraine. This event had global repercussions, especially on the political decisions of European countries. As expected, the role of Italy in the conflict became a major campaign issue for the Italian General Election held on 25 September 2022. Politicians frequently use Twitter to communicate during political campaigns, but bots often interfere and attempt to manipulate elections. Hence, understanding whether bots influenced public opinion regarding the conflict and, therefore, the elections is essential.

In this work, we investigate how Italian politics responded to the Russo-Ukrainian conflict on Twitter and whether bots manipulated public opinion before the 2022 general election. We first analyze 39,611 tweet of six major political Italian parties to understand how they discussed the war during the period February-December 2022. Then, we focus on the 360,823 comments under the last month’s posts before the elections, discovering around 12% of the commenters are bots. By examining their activities, it becomes clear they both distorted how war topics were treated and influenced real users during the last month before the elections.

F. Faveri and L. Cosuti—Authors contributed equally.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We computed the word clouds using WordCloud Python Library [42].

  2. 2.

    We used the model distilbert-multilingual-nli-stsb-quora-ranking.

  3. 3.

    The cosine-similarity was computed according to the formula in [61].

References

  1. Aiello, L.M., Deplano, M., Schifanella, R., Ruffo, G.: People are strange when you’re a stranger: impact and influence of bots on social networks. In: International Conference on Web and Social Media (ICWSM), vol. 6, pp. 10–17 (2012)

    Google Scholar 

  2. Alothali, E., Zaki, N., Mohamed, E.A., Alashwal, H.: Detecting social bots on twitter: a literature review. In: 2018 International Conference on Innovations in Information Technology (IIT), pp. 175–180. IEEE (2018)

    Google Scholar 

  3. Alsmadi, I., O’Brien, M.J.: How many bots in Russian troll tweets? Inf. Process. Manag. 57(6), 102303 (2020)

    Article  Google Scholar 

  4. Antonakaki, D., Fragopoulou, P., Ioannidis, S.: A survey of twitter research: data model, graph structure, sentiment analysis and attacks. Expert Syst. Appl. 164, 114006 (2021)

    Article  Google Scholar 

  5. Arias, M., Arratia, A., Xuriguera, R.: Forecasting with twitter data. ACM Trans. Intell. Syst. Technol. 5(1) (2014). https://doi.org/10.1145/2542182.2542190

  6. Bardi, S., Conti, M., Pajola, L., Tricomi, P.P.: Social honeypot for humans: luring people through self-managed Instagram pages. In: Tibouchi, M., Wang, X. (eds.) ACNS 2023. LNCS, vol. 13905, pp. 309–336. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-33488-7_12

    Chapter  Google Scholar 

  7. Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: International Conference on Web and Social Media (ICWSM), vol. 3, pp. 361–362 (2009)

    Google Scholar 

  8. Bessi, A., Ferrara, E.: Social bots distort the 2016 US presidential election online discussion. First Monday 21(11–7) (2016)

    Google Scholar 

  9. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)

    Article  MATH  Google Scholar 

  10. Broniatowski, D.A., et al.: Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. Am. J. Public Health 108(10), 1378–1384 (2018)

    Article  Google Scholar 

  11. Caravaca, F., González-Cabañas, J., Cuevas, Á., Cuevas, R.: Estimating ideology and polarization in European countries using Facebook data. EPJ Data Sci. 11(1), 56 (2022)

    Article  Google Scholar 

  12. Chang, H.C.H., Chen, E., Zhang, M., Muric, G., Ferrara, E.: Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436 (2021)

  13. Chavoshi, N., Hamooni, H., Mueen, A.: Identifying correlated bots in Twitter. In: Spiro, E., Ahn, Y.-Y. (eds.) SocInfo 2016. LNCS, vol. 10047, pp. 14–21. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47874-6_2

    Chapter  Google Scholar 

  14. Cresci, S.: A decade of social bot detection. Commun. ACM 63(10), 72–83 (2020)

    Article  Google Scholar 

  15. 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), 1–27 (2019)

    Article  Google Scholar 

  16. Twitter’s Developer: Twitter API platform. https://developer.twitter.com/en/docs

  17. Diaz, G.: stopwords-it (2022). https://github.com/stopwords-iso/stopwords-it

  18. Dutta, H.S., Chakraborty, T.: Blackmarket-driven collusion among retweeters-analysis, detection, and characterization. IEEE Trans. Inf. Forensics Secur. 15, 1935–1944 (2019)

    Article  Google Scholar 

  19. Fernquist, J., Kaati, L., Schroeder, R.: Political bots and the swedish general election. In: 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 124–129. IEEE (2018)

    Google Scholar 

  20. Ferrara, E.: Bots, elections, and social media: a brief overview. In: Shu, K., Wang, S., Lee, D., Liu, H. (eds.) Disinformation, Misinformation, and Fake News in Social Media. LNSN, pp. 95–114. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42699-6_6

    Chapter  Google Scholar 

  21. Ferrara, E., Chang, H., Chen, E., Muric, G., Patel, J.: Characterizing social media manipulation in the 2020 US presidential election. First Monday (2020)

    Google Scholar 

  22. Filter, J.: Clean-text (2022). https://github.com/jfilter/clean-text

  23. Gaubatz, K.T.: Elections and War: The Electoral Incentive in the Democratic Politics of War and Peace. Stanford University Press, Redwood City (1999)

    Google Scholar 

  24. Social feed manager (2016). https://doi.org/10.5281/zenodo.3784836

  25. Giakatos, D.P., Sermpezis, P., Vakali, A.: Pypoll: a python library automating mining of networks, discussions and polarization on twitter. arXiv preprint arXiv:2303.06478 (2023)

  26. Gilani, Z., Farahbakhsh, R., Tyson, G., Wang, L., Crowcroft, J.: Of bots and humans (on twitter). In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 349–354 (2017)

    Google Scholar 

  27. Grootendorst, M.: Bertopic: neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794 (2022)

  28. Insider, B.: Percentage of Bots on Twitter. https://www.businessinsider.com/twitter-bots-comprise-less-than-5-but-tweet-more-2022-9

  29. Jacomy, M., Venturini, T., Heymann, S., Bastian, M.: Forceatlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS ONE 9(6), e98679 (2014)

    Article  Google Scholar 

  30. James Raynolds, B.: Italy pm conte vows more united italy as salvini leaves power. Italy PM Conte vows more united Italy as Salvini leaves power

    Google Scholar 

  31. Khan, A., et al.: Election prediction on twitter: a systematic mapping study. Complexity 2021, 1–27 (2021)

    Google Scholar 

  32. Linvill, D.L., Boatwright, B.C., Grant, W.J., Warren, P.L.: “The Russians are hacking my brain!” investigating Russia’s internet research agency twitter tactics during the 2016 united states presidential campaign. Comput. Hum. Behav. 99, 292–300 (2019)

    Google Scholar 

  33. Lorenzo-Luaces, L., et al.: Sociodemographics and transdiagnostic mental health symptoms in social (studies of online cohorts for internalizing symptoms and language) I and II: cross-sectional survey and botometer analysis. JMIR Formative Res. 6(10), e39324 (2022)

    Article  Google Scholar 

  34. Ludovico, M.: Cybersecurity, 2022 annus horribilis: 13mila attacchi, +138%. Il Sole 24 Ore (2023)

    Google Scholar 

  35. Mannocci, L., Cresci, S., Monreale, A., Vakali, A., Tesconi, M.: Mulbot: unsupervised bot detection based on multivariate time series. arXiv preprint arXiv:2209.10361 (2022)

  36. Martini, F., Samula, P., Keller, T.R., Klinger, U.: Bot, or not? Comparing three methods for detecting social bots in five political discourses. Big Data Soc. 8(2), 20539517211033570 (2021)

    Article  Google Scholar 

  37. Mattei, M., Caldarelli, G., Squartini, T., Saracco, F.: Italian twitter semantic network during the Covid-19 epidemic. EPJ Data Sci. 10(1), 47 (2021)

    Article  Google Scholar 

  38. Mazza, M., Avvenuti, M., Cresci, S., Tesconi, M.: Investigating the difference between trolls, social bots, and humans on twitter. Comput. Commun. 196, 23–36 (2022)

    Article  Google Scholar 

  39. McInnes, L., Healy, J., Astels, S.: HDBSCAN: hierarchical density based clustering. J. Open Source Softw. 2(11), 205 (2017)

    Article  Google Scholar 

  40. McInnes, L., Healy, J., Melville, J.: Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)

  41. Messia, H., Angela Dewan, C.: Italian prime minister giuseppe conte resigns, in calculated move amid coronavirus crisis (2021). https://edition.cnn.com/2021/01/26/europe/italy-giuseppe-conte-resignation-intl/index.html

  42. Oesper, L., Merico, D., Isserlin, R., Bader, G.D.: Wordcloud: a cytoscape plugin to create a visual semantic summary of networks. Source Code Biol. Med. 6(1), 7 (2011)

    Article  Google Scholar 

  43. Pastor-Galindo, J., et al.: Spotting political social bots in twitter: a use case of the 2019 spanish general election. IEEE Trans. Netw. Serv. Manage. 17(4), 2156–2170 (2020)

    Article  Google Scholar 

  44. Rauchfleisch, A., Kaiser, J.: The false positive problem of automatic bot detection in social science research. PLoS ONE 15(10), e0241045 (2020)

    Article  Google Scholar 

  45. Rehurek, R., Sojka, P.: Gensim-python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic, vol. 3, no. 2 (2011)

    Google Scholar 

  46. Reimers, N., Gurevych, I.: Making monolingual sentence embeddings multilingual using knowledge distillation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2020). https://arxiv.org/abs/2004.09813

  47. Gazzetta Ufficiale della Repubblica italiana: Decreto legge 28 febbraio 2022 (2022). https://www.gazzettaufficiale.it/eli/gu/2022/02/28/49/sg/pdf

  48. Dubbin, R.: Percentage of Bots on the early stages of Twitter. https://www.newyorker.com/tech/annals-of-technology/the-rise-of-twitter-bots

  49. Sartori, E., Pajola, L., Da San Martino, G., Conti, M.: The impact of Covid-19 on online discussions: the case study of the sanctioned suicide forum. In: Proceedings of the ACM Web Conference 2023, pp. 4060–4064 (2023)

    Google Scholar 

  50. Schäfer, F., Evert, S., Heinrich, P.: Japan’s 2014 general election: political bots, right-wing internet activism, and prime minister Shinzō Abe’s hidden nationalist agenda. Big Data 5(4), 294–309 (2017)

    Article  Google Scholar 

  51. Shevtsov, A., Tzagkarakis, C., Antonakaki, D., Ioannidis, S.: Identification of twitter bots based on an explainable machine learning framework: the US 2020 elections case study. In: International Conference on Web and Social Media (ICWSM), vol. 16, pp. 956–967 (2022)

    Google Scholar 

  52. Singh, M., Bansal, D., Sofat, S.: Behavioral analysis and classification of spammers distributing pornographic content in social media. Soc. Netw. Anal. Min. 6(1), 1–18 (2016). https://doi.org/10.1007/s13278-016-0350-0

    Article  Google Scholar 

  53. Stefanoni, F.: Un anno di guerra in ucraina, la risposta della politica italiana: le posizioni (e le evoluzioni) dei partiti. Corriere della Sera (2023)

    Google Scholar 

  54. Tahmasbi, F., et al.: “go eat a bat, chang!”: On the emergence of sinophobic behavior on web communities in the face of Covid-19. In: Proceedings of the Web Conference 2021, pp. 1122–1133 (2021)

    Google Scholar 

  55. Tricomi, P.P., Tarahomi, S., Cattai, C., Martini, F., Conti, M.: Are we all in a truman show? spotting instagram crowdturfing through self-training. arXiv preprint arXiv:2206.12904 (2022)

  56. Varol, O.: Should we agree to disagree about twitter’s bot problem? arXiv preprint arXiv:2209.10006 (2022)

  57. Vasilkova, V., Legostaeva, N.: Social bots in political communication. RUDN J. Sociol. 19(1), 121–133 (2019)

    Article  Google Scholar 

  58. Institute for the Study of War: Ukraine conflict updates 2022 (2022). https://www.understandingwar.org/backgrounder/ukraine-conflict-updates-2022

  59. Weber, I., Garimella, V.R.K., Batayneh, A.: Secular vs. islamist polarization in egypt on twitter. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 290–297 (2013)

    Google Scholar 

  60. Weng, Z., Lin, A.: Public opinion manipulation on social media: social network analysis of twitter bots during the Covid-19 pandemic. Int. J. Environ. Res. Public Health 19(24), 16376 (2022)

    Article  Google Scholar 

  61. Wikipedia: Cosine similarity (2023). https://en.wikipedia.org/wiki/Cosine_similarity

  62. Wikipedia: Timeline of the 2022 Russian invasion of Ukraine (2023). https://en.wikipedia.org/wiki/Timeline_of_the_2022_Russian_invasion_of_Ukraine

  63. Woolley, S.C.: Automating power: social bot interference in global politics. First Monday (2016)

    Google Scholar 

  64. Yang, K.C., Ferrara, E., Menczer, F.: Botometer 101: social bot practicum for computational social scientists. J. Comput. Soc. Sci. 1–18 (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Padua, Padua, Italy

    Francesco Luigi De Faveri, Luca Cosuti, Pier Paolo Tricomi & Mauro Conti

  2. Chisito S.r.l, Padua, Italy

    Pier Paolo Tricomi & Mauro Conti

Authors
  1. Francesco Luigi De Faveri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luca Cosuti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pier Paolo Tricomi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mauro Conti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pier Paolo Tricomi .

Editor information

Editors and Affiliations

  1. University of Kent, Canterbury, UK

    Budi Arief

  2. University of Pisa, Pisa, Italy

    Anna Monreale

  3. Cyprus University of Technology, Limassol, Cyprus

    Michael Sirivianos

  4. University of Kent, Canterbury, UK

    Shujun Li

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

De Faveri, F.L., Cosuti, L., Tricomi, P.P., Conti, M. (2023). Twitter Bots Influence on the Russo-Ukrainian War During the 2022 Italian General Elections. In: Arief, B., Monreale, A., Sirivianos, M., Li, S. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2023. Lecture Notes in Computer Science, vol 14097. Springer, Singapore. https://doi.org/10.1007/978-981-99-5177-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5177-2_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5176-5

  • Online ISBN: 978-981-99-5177-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation