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Information Disorder

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New Dimensions of Information Warfare

Part of the book series: Advances in Information Security ((ADIS,volume 84))

Abstract

The rise of new technologies, including Online Social Network (OSN)s, media sharing services, online discussion boards, and online instant messaging applications, make information production and propagation increasingly fast.

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Notes

  1. 1.

    https://techterms.com/definition/troll (Last checked August 2020).

  2. 2.

    https://guardianlv.com/2013/11/china-uses-an-army-of-sockpuppets-to-control-public-opinion-and-the-us-will-too/ (Last checked August 2020).

  3. 3.

    https://en.wikipedia.org/wiki/Sockpuppet(Internet) (Last checked August 2020).

  4. 4.

    https://www.yourdictionary.com/meat-puppet (Last checked August 2020).

  5. 5.

    https://en.wikipedia.org/wiki/Astroturfing (Last checked August 2020).

  6. 6.

    https://www.thefridaytimes.com/political-astroturf/ (Last checked August 2020).

  7. 7.

    https://www.washingtonpost.com/news/the-fix/wp/2018/04/03/a-new-study-suggests-fake-news-might-have-won-donald-trump-the-2016-election/?noredirect=on&utm_term=.d6e63f61fa06 (Last checked August 2020).

  8. 8.

    https://reports.weforum.org/outlook-14/top-ten-trends-category-page/10-the-rapid-spread-of-misinformation-online/?doing_wp_cron=1583915074.7138180732727050781250 (Last checked August 2020).

  9. 9.

    https://outride.rs/en/vaccines-fake-news/ (Last checked August 2020).

  10. 10.

    https://www.who.int/news-room/feature-stories/ten-threats-to-global-health-in-2019 (Last checked August 2020).

  11. 11.

    https://www.vaccines.gov/getting/for_parents/five_reasons (Last checked August 2020).

  12. 12.

    https://www.who.int/news-room/facts-in-pictures/detail/immunization (Last checked August 2020).

  13. 13.

    https://en.wikipedia.org/wiki/Vaccine_hesitancy (Last checked August 2020).

  14. 14.

    https://www.euro.who.int/en/health-topics/disease-prevention/vaccines-and-immunization/publications/surveillance-and-data/who-epidata (Last checked August 2020).

  15. 15.

    https://medium.com/s/world-wide-wtf/how-the-internet-made-us-believe-in-a-flat-earth-2e42c3206223 (Last checked August 2020).

  16. 16.

    https://www.forbes.com/sites/trevornace/2017/03/28/shaq-thinks-earth-is-flat-because-it-doesnt-go-up-and-down-when-he-drives/#4ab8f4187233 (Last checked August 2020).

  17. 17.

    https://edition.cnn.com/2019/01/25/tech/youtube-conspiracy-video-recommendations/index.html (Last checked August 2020).

  18. 18.

    https://climate.nasa.gov/faq/12/whats-the-difference-between-climate-change-and-global-warming/ (Last checked August 2020).

  19. 19.

    https://www.bbc.com/news/world-us-canada-51213003 (Last checked August 2020).

  20. 20.

    https://usa.usembassy.de/etexts/gov/democracy-elections.htm (Last checked August 2020).

  21. 21.

    https://blogs.microsoft.com/on-the-issues/2018/08/20/we-are-taking-new-steps-against-broadening-threats-to-democracy/ (Last checked August 2020).

  22. 22.

    http://techpresident.com/news/25374/bad-news-bots-how-civil-society-can-combat-automated-online-propaganda (Last checked August 2020).

  23. 23.

    https://www.independent.co.uk/news/twitter-most-tweeted-moments-2016-donald-trump-brexit-black-lives-matter-rio-a7466236.html (Last checked August 2020).

  24. 24.

    https://www.journalism.org/2016/05/26/news-use-across-social-media-platforms-2016/ (Last checked August 2020).

  25. 25.

    https://www.buzzfeednews.com/article/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook (Last checked August 2020).

  26. 26.

    https://www.buzzfeednews.com/article/craigsilverman/fake-news-survey (Last checked August 2020).

  27. 27.

    http://mib.projects.iit.cnr.it/dataset.html (Last checked August 2020).

  28. 28.

    https://www.wired.com/story/facebook-may-have-more-russian-troll-farms-to-worry-about/ (Last checked August 2020).

  29. 29.

    https://www.snopes.com/fact-check/pope-francis-donald-trump-endorsement/ (Last checked August 2020).

  30. 30.

    https://www.snopes.com/fact-check/hillary-clinton-bought-137-million-worth-of-illegal-arms/ (Last checked August 2020).

  31. 31.

    https://www.snopes.com/fact-check/wikileaks-clintons-purchase-200-million-maldives-estate/ (Last checked August 2020).

  32. 32.

    https://www.inquisitr.com/3682274/hillary-clintons-alleged-lolita-child-pedophile-sex-slave-island-ring-scandal-5th-of-november-part-1-claims-by-anonymous/ (Last checked August 2020).

  33. 33.

    https://blogs.microsoft.com/on-the-issues/2018/01/02/today-technology-top-ten-tech-issues-2018/ (Last checked August 2020).

  34. 34.

    https://blogs.microsoft.com/on-the-issues/2018/04/13/announcing-the-defending-democracy-program/ (Last checked August 2020).

  35. 35.

    https://www.politifact.com/ (Last checked August 2020).

  36. 36.

    https://www.factcheck.org/ (Last checked August 2020).

  37. 37.

    https://www.snopes.com/ (Last checked August 2020).

  38. 38.

    https://fullfact.org/ (Last checked August 2020).

  39. 39.

    https://www.hoax-slayer.net/ (Last checked August 2020).

  40. 40.

    https://www.truthorfiction.com/.

  41. 41.

    https://fiskkit.com/ (Last checked August 2020).

  42. 42.

    https://github.com/BuzzFeedNews/2016-10-facebook-fact-check (Last checked August 2020).

  43. 43.

    https://github.com/FakeNewsChallenge/fnc-1 (Last checked August 2020).

  44. 44.

    https://github.com/BuzzFeedNews/2017-12-fake-news-top-50 (Last checked August 2020).

  45. 45.

    https://zenodo.org/record/3375714 (Last checked August 2020).

  46. 46.

    https://www.uvic.ca/engineering/ece/isot/datasets/ (Last checked August 2020).

  47. 47.

    https://www.kaggle.com/c/fake-news/data (Last checked August 2020).

  48. 48.

    https://www.cnbc.com/2017/03/10/nearly-48-million-twitter-accounts-could-be-bots-says-study.html (Last checked August 2020).

  49. 49.

    https://variety.com/2019/digital/news/facebook-took-down-2-2-billion-fake-accounts-in-q1-1203224487/ (Last checked August 2020).

  50. 50.

    https://firstdraftnews.org/latest/the-not-so-simple-science-of-social-media-bots/ (Last checked August 2020).

  51. 51.

    https://www.technologyreview.com/2020/05/21/1002105/covid-bot-twitter-accounts-push-to-reopen-america/ (Last checked August 2020).

  52. 52.

    https://www.cs.unm.edu/~chavoshi/debot/ (Last checked August 2020).

  53. 53.

    https://www.cs.unm.edu/~chavoshi/debot/api.html (Last checked August 2020).

  54. 54.

    https://www.cs.unm.edu/~chavoshi/debot/on_demand.html (Last checked August 2020).

  55. 55.

    https://github.com/mkearney/tweetbotornot2 (Last checked August 2020).

  56. 56.

    https://pan.webis.de/clef19/pan19-web/author-profiling.html (Last checked August 2020).

  57. 57.

    https://botometer.iuni.iu.edu/bot-repository/datasets.html (Last checked August 2020).

  58. 58.

    https://arstechnica.com/tech-policy/2019/12/social-media-platforms-leave-95-of-reported-fake-accounts-up-study-finds/ (Last checked August 2020).

  59. 59.

    http://mib.projects.iit.cnr.it/dataset.html (Last checked August 2020).

  60. 60.

    https://www.bloomberg.com/news/articles/2012-05-24/likejacking-spammers-hit-social-media (Last checked August 2020).

  61. 61.

    https://www.dictionary.com/browse/meme?s=t (Last checked August 2020).

  62. 62.

    https://en.wikipedia.org/wiki/Ted_Cruz%E2%80%93Zodiac_Killer_meme (Last checked August 2020).

  63. 63.

    https://www.warrington-worldwide.co.uk/2020/04/10/the-effects-of-internet-trolling/#:~:text=Some%20of%20the%20feelings%20that,the%20person%20disinterest%20in%20life (Last checked August 2020).

  64. 64.

    https://about.fb.com/news/2018/12/inside-feed-coordinated-inauthentic-behavior/ (Last checked August 2020).

References

  1. How to escape your political bubble for a clearer view. https://www.nytimes.com/2017/03/03/arts/the-battle-over-your-political-bubble.html. (Last checked August 2020)

  2. S. Cresci, A decade of social bot detection, Communications of the ACM (Forthcoming) (2020)

    Google Scholar 

  3. A. Marwick, R. Lewis, Media Manipulation and Disinformation Online (Data and Society Research Institute, New York 2017)

    Google Scholar 

  4. J. Yan, Bot, cyborg and automated turing test, in The 2006 International Workshop on Security Protocols (Springer, Berlin, 2006), pp. 190–197

    Google Scholar 

  5. Z. Chu, S. Gianvecchio, H. Wang, S. Jajodia, Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans. Dependable Secure Comput. 9, 811–824 (2012)

    Article  Google Scholar 

  6. E. Ferrara, The history of digital spam. Commun. ACM 62(8), 82–91 (2019)

    Article  Google Scholar 

  7. B. Waugh, M. Abdipanah, O. Hashemi, S.A. Rahman, D. M. Cook, The influence and deception of Twitter: the authenticity of the narrative and slacktivism in the Australian electoral process, in The 14th Australian Information Warfare Conference (AIWC’13) (2013)

    Google Scholar 

  8. J. Ratkiewicz, M.D. Conover, M. Meiss, B. Gonçalves, A. Flammini, F.M. Menczer, Detecting and tracking political abuse in social media, in The Fifth International AAAI Conference on Weblogs and Social Media (ICWSM’11) (AAAI, 2011)

    Google Scholar 

  9. G. Da San Martino, S. Cresci, A. Barrón-Cede no, S. Yu, R. Di Pietro, P. Nakov, A survey on computational propaganda detection, in The 29th International Joint Conference on Artificial Intelligence (IJCAI’20) (2020)

    Google Scholar 

  10. N. Persily, The 2016 US Election: can democracy survive the Internet? J. Democr. 28(2), 63–76 (2017)

    Article  Google Scholar 

  11. M. Mazza, S. Cresci, M. Avvenuti, W. Quattrociocchi, M. Tesconi, Rtbust: exploiting temporal patterns for botnet detection on twitter, in The 11th International Conference on Web Science (WebSci’19) (ACM, New York, 2019), pp. 183–192

    Google Scholar 

  12. A.M. Guess, M. Lerner, B. Lyons, J.M. Montgomery, B. Nyhan, J. Reifler, N. Sircar, A digital media literacy intervention increases discernment between mainstream and false news in the United States and India. Proc. Natl. Acad. Sci. 117(27), 15536–15545 (2020)

    Article  Google Scholar 

  13. D.K. Flaherty, The vaccine-autism connection: a public health crisis caused by unethical medical practices and fraudulent science. Ann. Pharmacother. 45(10), 1302–1304 (2011)

    Article  Google Scholar 

  14. C.A. Borella, D. Rossinelli, Fake news, immigration, and opinion polarization, in SocioEconomic Challenges (2017)

    Google Scholar 

  15. C. Garwood, Flat Earth: The History of an Infamous Idea (Pan Macmillan, 2008)

    Google Scholar 

  16. D.E. Allen, M. McAleer, Fake news and indifference to scientific fact: President Trump’s confused tweets on global warming, climate change and weather. Scientometrics 117(1), 625–629 (2018)

    Article  Google Scholar 

  17. S. Van der Linden, A. Leiserowitz, S. Rosenthal, E. Maibach, Inoculating the public against misinformation about climate change. Global Chall. 1(2), 1600008 (2017)

    Google Scholar 

  18. M. Gabielkov, A. Ramachandran, A. Chaintreau, A. Legout, Social clicks: what and who gets read on twitter? ACM SIGMETRICS Perform. Eval. Rev. 44(1), 179–192 (2016)

    Article  Google Scholar 

  19. R.M. Bond, C.J. Fariss, J.J. Jones, A.D. Kramer, C. Marlow, J.E. Settle, J.H. Fowler, A 61-million-person experiment in social influence and political mobilization. Nature 489(7415), 295 (2012)

    Google Scholar 

  20. C.A. Bail, B. Guay, E. Maloney, A. Combs, D.S. Hillygus, F. Merhout, D. Freelon, A. Volfovsky, Assessing the Russian internet research agency’s impact on the political attitudes and behaviors of American twitter users in late 2017. Proc. Natl. Acad. Sci. 117(1), 243–250 (2020)

    Article  Google Scholar 

  21. M. Rueda, 2012’s biggest social media blunders in LatAm politics. https://abcnews.go.com/ABC_Univision/ABC_Univision/2012s-biggest-social-media-blunders-latin-american-politics/story?id=18063022. Last checked August 2020

  22. T. Filer, R. Fredheim, Popular with the robots: accusation and automation in the argentine presidential elections, 2015. Int. J. Polit. Cult. Soc. 30(3), 259–274 (2017)

    Article  Google Scholar 

  23. T. Peel, The coalition’s twitter fraud and deception. https://independentaustralia.net/politics/politics-display/the-coalitions-twitter-fraud-and-deception,5660. Last checked August 2020

  24. E. Kusen, M. Strembeck, An analysis of the twitter discussion on the 2016 Austrian presidential elections (2017). arXiv preprint arXiv:1707.09939

    Google Scholar 

  25. H. Ellyatt, Us far-right activists, wikileaks and bots help amplify macron leaks. https://www.cnbc.com/2017/05/07/macron-email-leaks-far-right-wikileaks-twitter-bots.htm. Last checked August 2020.

  26. R. Brandom, Emails leaked in ’massive hacking attack’ on French presidential campaign. https://www.theverge.com/2017/5/5/15564532/macron-email-leak-russia-hacking-campaign-4chan. Last checked August 2020

  27. S. Almasy, Emmanuel macron’s French presidential campaign hacked. https://edition.cnn.com/2017/05/05/europe/france-election-macron-hack-allegation/index.html. Last checked August 2020

  28. E. Ferrara, Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday 22(8), 2017

    Google Scholar 

  29. C. Desigaud, P.N. Howard, S. Bradshaw, B. Kollanyi, G. Bolsover, Junk news and bots during the French presidential election: what are French voters sharing over twitter in round two? Tech. rep., COMPROP Data Memo, 2017

    Google Scholar 

  30. F. Brachten, S. Stieglitz, L. Hofeditz, K. Kloppenborg, A. Reimann, Strategies and influence of social bots in a 2017 German state election—a case study on twitter (2017). arXiv preprint arXiv:1710.07562

    Google Scholar 

  31. K. Kupferschmidt, Bot-hunters eye mischief in German election. Science 357(6356), 1081–1082 (2017)

    Article  Google Scholar 

  32. F. Morstatter, Y. Shao, A. Galstyan, S. Karunasekera, From alt-right to Alt-Rechts: twitter analysis of the 2017 German federal election, in Companion Proceedings of the Web Conference 2018 (WWW Companion’18), (IW3C2, 2018), pp. 621–628

    Google Scholar 

  33. T.R. Keller, U. Klinger, Social bots in election campaigns: theoretical, empirical, and methodological implications. Polit. Commun. 36(1), 171–189 (2019)

    Article  Google Scholar 

  34. A. Applebaum, P. Pomerantsev, M. Smith, C. Colliver, ‘make Germany great again’: Kremlin, alt-right, and international influences in the 2017 German elections, in London School of Economics (2017)

    Google Scholar 

  35. A. Vogt, Hot or bot? Italian professor casts doubt on politician’s twitter popularity. https://www.theguardian.com/world/2012/jul/22/bot-italian-politician-twitter-grillo. Last checked August 2020

  36. N. Squires, Human or ’bot’? doubts over Italian comic Beppe Grillo’s Twitter followers. https://www.telegraph.co.uk/technology/twitter/9421072/Human-or-bot-Doubts-over-Italian-comic-Beppe-Grillos-Twitter-followers.html. Last checked August 2020

  37. C. Albanese, Now bots are trying to help populists win Italy’s election. https://www.bloomberg.com/news/articles/2018-02-19/now-bots-are-trying-to-help-populists-win-italy-s-election. Last checked August 2020

  38. DFRLab, #electionwatch: Italy’s self-made bots. https://medium.com/dfrlab/electionwatch-italys-self-made-bots-200e2e268d0e. Last checked August 2020

  39. TheLocal, Facebook shuts down more than 20 ’fake news’ pages in Italy. https://www.thelocal.it/20190513/facebook-shuts-down-more-than-20-fake-news-pages-in-italy. Last checked August 2020

  40. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, M. Tesconi, 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’17 Companion), IW3C2 (2017), pp. 963–972

    Google Scholar 

  41. M. Orcutt, Twitter mischief plagues Mexico’s election. https://www.technologyreview.com/s/428286/twitter-mischief-plagues-mexicos-election/. Last checked August 2020

  42. M. Glowacki, V. Narayanan, S. Maynard, G. Hirsch, B. Kollanyi, L. Neudert, V. Barash, et al., News and political information consumption in Mexico: mapping the 2018 Mexican presidential election on twitter and Facebook. The Computational Propaganda Project, 2018

    Google Scholar 

  43. J. Robertson, M. Riley, A. Willis, How to hack an election. https://www.bloomberg.com/features/2016-how-to-hack-an-election/. Last checked August 2020

  44. E. Gallagher, Mexican botnet dirty wars: bots are waging a dirty war in Mexican social media. https://www.youtube.com/watch?v=I3D3iIZGSt8. Last checked August 2020

  45. T. Guardian, ’i’ve had enough’, says Mexican attorney general in missing students gaffe. https://www.theguardian.com/world/2014/nov/09/protests-flare-in-mexico-after-attorney-generals-enough-im-tired-remarks. Last checked August 2020

  46. P. Suárez-Serrato, M.E. Roberts, C. Davis, F. Menczer, On the influence of social bots in online protests, in Social Informatics (Springer, Cham, 2016), pp. 269–278

    Google Scholar 

  47. M. Stella, E. Ferrara, M. De Domenico, Bots sustain and inflate striking opposition in online social systems (2018). arXiv preprint arXiv:1802.07292

    Google Scholar 

  48. D. Stukal, S. Sanovich, R. Bonneau, J.A. Tucker, Detecting bots on Russian political twitter. Big Data 5(4), 310–324 (2017)

    Article  Google Scholar 

  49. S. Zannettou, B. Bradlyn, E. De Cristofaro, G. Stringhini, J. Blackburn, Characterizing the use of images by state-sponsored troll accounts on Twitter (2019). arXiv preprint arXiv:1901.05997

    Google Scholar 

  50. S. Zannettou, T. Caulfield, W. Setzer, M. Sirivianos, G. Stringhini, J. Blackburn, Who let the trolls out? Towards understanding state-sponsored trolls, in Proceedings of the 10th ACM Conference on Web Science (ACM, New York, 2019), pp. 353–362

    Google Scholar 

  51. L.G. Stewart, A. Arif, K. Starbird, Examining trolls and polarization with a retweet network, in Proceedings of the ACM WSDM, Workshop on Misinformation and Misbehavior Mining on the Web (2018)

    Google Scholar 

  52. S. Shane, V. Goel, Fake Russian Facebook accounts bought $100,000 in political ads. https://www.nytimes.com/2017/09/06/technology/facebook-russian-political-ads.html. Last checked August 2020

  53. R. Dutt, A. Deb, E. Ferrara, ‘senator, we sell ads’: analysis of the 2016 Russian Facebook ads campaign, in International Conference on Intelligent Information Technologies (Springer, Berlin, 2018), pp. 151–168

    Google Scholar 

  54. E. Poyrazlar, Turkey’s leader bans his own twitter bot army. https://www.vocativ.com/world/turkey-world/turkeys-leader-nearly-banned-twitter-bot-army/. Last checked August 2020

  55. S. Hegelich, D. Janetzko, Are social bots on twitter political actors? Empirical evidence from a Ukrainian social botnet, in Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016) (2016), pp. 579–582

    Google Scholar 

  56. P.N. Howard, B. Kollanyi, Bots,#strongerin, and#brexit: computational propaganda during the UK-EU referendum. Available at SSRN 2798311 (2016)

    Google Scholar 

  57. M.T. Bastos, D. Mercea, The Brexit botnet and user-generated hyperpartisan news. Soc. Sci. Comput. Rev. 37(1), 38–54 (2019)

    Article  Google Scholar 

  58. C. Llewellyn, L. Cram, R.L. Hill, A. Favero, For whom the bell trolls: shifting troll behaviour in the twitter Brexit debate. J. Common Market Stud. 57(5), 1148–1164 (2019)

    Article  Google Scholar 

  59. H. Allcott, M. Gentzkow, Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–36 (2017)

    Article  Google Scholar 

  60. A. Guess, B. Nyhan, J. Reifler, Selective exposure to misinformation: evidence from the consumption of fake news during the 2016 US presidential campaign. Eur. Res. Council 9(3), 4 (2018)

    Google Scholar 

  61. A. Bessi and E. Ferrara, Social bots distort the 2016 US presidential election online discussion. First Monday 21(11-7) (2016)

    Google Scholar 

  62. C. Shao, G.L. Ciampaglia, O. Varol, A. Flammini, F. Menczer, The spread of fake news by social bots. 96, 104 (2017). arXiv preprint arXiv:1707.07592

    Google Scholar 

  63. W. Samuel, H. Phil, Bots unite to automate the presidential election. https://www.wired.com/2016/05/twitterbots-2/. Last checked August 2020

  64. B. Ryan, Nearly half of Donald Trump’s twitter followers are fake accounts and bots. https://www.newsweek.com/donald-trump-twitter-followers-fake-617873. Last checked August 2020

  65. A. Fourney, M.Z. Racz, G. Ranade, M. Mobius, E. Horvitz, Geographic and temporal trends in fake news consumption during the 2016 US presidential election, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (ACM, New York, 2017), pp. 2071–2074

    Google Scholar 

  66. E. Mustafaraj, P.T. Metaxas, From obscurity to prominence in minutes: political speech and real-time search (2010)

    Google Scholar 

  67. A. Badawy, E. Ferrara, K. Lerman, Analyzing the digital traces of political manipulation: the 2016 Russian interference twitter campaign, in 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE, Piscataway, 2018), pp. 258–265

    Google Scholar 

  68. A. Badawy, K. Lerman, E. Ferrara, Who falls for online political manipulation? in Companion Proceedings of The 2019 World Wide Web Conference (ACM, New York, 2019), pp. 162–168

    Book  Google Scholar 

  69. M. Jensen, Russian trolls and fake news: information or identity logics? J. Int. Aff. 71(1.5), 115–124 (2018)

    Google Scholar 

  70. M. Forelle, P. Howard, A. Monroy-Hernández, S. Savage, Political bots and the manipulation of public opinion in Venezuela (2015). arXiv preprint arXiv:1507.07109

    Google Scholar 

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

    Article  Google Scholar 

  72. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, M. Tesconi, DNA-inspired online behavioral modeling and its application to spambot detection. IEEE Intell. Syst. 31(5), 58–64 (2016)

    Article  Google Scholar 

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

    Google Scholar 

  74. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, M. Tesconi, Exploiting digital DNA for the analysis of similarities in twitter behaviours, in 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (IEEE, Piscataway, 2017), pp. 686–695

    Google Scholar 

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

    Article  Google Scholar 

  76. A. Spangher, G. Ranade, B. Nushi, A. Fourney, E. Horvitz, Analysis of strategy and spread of Russia-sponsored content in the US in 2017 (2018). arXiv preprint arXiv:1810.10033

    Google Scholar 

  77. S. Zannettou, T. Caulfield, E. De Cristofaro, M. Sirivianos, G. Stringhini, J. Blackburn, Disinformation warfare: understanding state-sponsored trolls on twitter and their influence on the web, in Companion Proceedings of the 2019 World Wide Web Conference (ACM, New York, 2019), pp. 218–226

    Google Scholar 

  78. B.S. Bello, R. Heckel, Analyzing the behaviour of twitter bots in post Brexit politics

    Google Scholar 

  79. O. Solon, Facebook’s fake news: Mark Zuckerberg rejects ‘crazy idea’ that it swayed voters. https://www.theguardian.com/technology/2016/nov/10/facebook-fake-news-us-election-mark-zuckerberg-donald-trump. Last checked August 2020

  80. R. Max, Donald Trump won because of facebook. https://nymag.com/intelligencer/2016/11/donald-trump-won-because-of-facebook.html. Last checked August 2020

  81. C. Dewey, Facebook fake-news writer: ’i think Donald Trump is in the white house because of me’. https://www.washingtonpost.com/news/the-intersect/wp/2016/11/17/facebook-fake-news-writer-i-think-donald-trump-is-in-the-white-house-because-of-me/. Last checked August 2020

  82. N. Mele, D. Lazer, M. Baum, N. Grinberg, L. Friedland, K. Joseph, W. Hobbs, C. Mattsson, Combating fake news: an agenda for research and action (2017). Di https://www.hks.harvard.edu/publications/combating-fake-news-agenda-research-and-action (Retrieved October 17, 2018)

  83. Z. Jin, J. Cao, Y. Zhang, J. Zhou, Q. Tian, Novel visual and statistical image features for microblogs news verification. IEEE Trans. Multimedia 19(3), 598–608 (2016)

    Article  Google Scholar 

  84. C. Castillo, M. Mendoza, B. Poblete, Information credibility on twitter, in Proceedings of the 20th International Conference on World Wide Web (2011), pp. 675–684

    Google Scholar 

  85. S. Vosoughi, M. Mohsenvand, D. Roy, Rumor gauge: predicting the veracity of rumors on twitter. ACM Trans. Knowl. Discov. Data 11(4), 1–36 (2017)

    Article  Google Scholar 

  86. N. Hassan, F. Arslan, C. Li, M. Tremayne, Toward automated fact-checking: detecting check-worthy factual claims by claimbuster, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017), pp. 1803–1812

    Google Scholar 

  87. G. Karadzhov, P. Nakov, L. Màrquez, A. Barrón-Cede no, I. Koychev, Fully automated fact checking using external sources (2017). arXiv preprint arXiv:1710.00341

    Google Scholar 

  88. K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsl. 19(1), 22–36 (2017)

    Article  Google Scholar 

  89. G. Pennycook, D.G. Rand, Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc. Nat. Acad. Sci. 116(7), 2521–2526 (2019)

    Article  Google Scholar 

  90. M.R. Pinto, Y.O. de Lima, C.E. Barbosa, J.M. de Souza, Towards fact-checking through crowdsourcing, in 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD) (IEEE, Piscataway, 2019), pp. 494–499

    Google Scholar 

  91. S.M. Mohammad, P. Sobhani, S. Kiritchenko, Stance and sentiment in tweets. ACM Trans. Internet Tech. 17(3), 1–23 (2017)

    Article  Google Scholar 

  92. Y. Yamaguchi, T. Takahashi, T. Amagasa, H. Kitagawa, Turank: twitter user ranking based on user-tweet graph analysis, in Web Information Systems Engineering—WISE 2010, ed. by L. Chen, P. Triantafillou, T. Suel (Springer, Berlin, 2010), pp. 240–253

    Chapter  Google Scholar 

  93. B. Rath, W. Gao, J. Ma, J. Srivastava, From retweet to believability: utilizing trust to identify rumor spreaders on twitter, in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (2017), pp. 179–186

    Google Scholar 

  94. J. Zhang, J. Tang, J. Li, Expert finding in a social network, in International Conference on Database Systems for Advanced Applications (Springer, Berlin, 2007), pp. 1066–1069

    Google Scholar 

  95. A. Bozzon, M. Brambilla, S. Ceri, M. Silvestri, G. Vesci, Choosing the right crowd: expert finding in social networks, in Proceedings of the 16th International Conference on Extending Database Technology (2013), pp. 637–648

    Google Scholar 

  96. R.M. Tripathy, A. Bagchi, S. Mehta, A study of rumor control strategies on social networks, in Proceedings of the 19th ACM International Conference on Information and Knowledge Management (2010), pp. 1817–1820

    Google Scholar 

  97. N.P. Nguyen, G. Yan, M.T. Thai, S. Eidenbenz, Containment of misinformation spread in online social networks, in Proceedings of the Fourth Annual ACM Web Science Conference (2012), pp. 213–222

    Google Scholar 

  98. T. Mitra, E. Gilbert, Credbank: a large-scale social media corpus with associated credibility annotations, in Ninth International AAAI Conference on Web and Social Media (2015)

    Google Scholar 

  99. W.Y. Wang, “liar, liar pants on fire”: a new benchmark dataset for fake news detection (2017). arXiv preprint arXiv:1705.00648

    Google Scholar 

  100. E. Tacchini, G. Ballarin, M.L. Della Vedova, S. Moret, L. de Alfaro, Some like it hoax: automated fake news detection in social networks (2017). arXiv preprint arXiv:1704.07506

    Google Scholar 

  101. G.C. Santia, J.R. Williams, Buzzface: a news veracity dataset with Facebook user commentary and egos, in Twelfth International AAAI Conference on Web and Social Media (2018)

    Google Scholar 

  102. J. Golbeck, M. Mauriello, B. Auxier, K.H. Bhanushali, C. Bonk, M.A. Bouzaghrane, C. Buntain, R. Chanduka, P. Cheakalos, J.B. Everett, et al., Fake news vs satire: a dataset and analysis, in Proceedings of the 10th ACM Conference on Web Science (ACM, New York, 2018), pp. 17–21

    Google Scholar 

  103. K. Shu, D. Mahudeswaran, S. Wang, D. Lee, H. Liu, Fakenewsnet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media (2018). arXiv preprint arXiv:1809.01286

    Google Scholar 

  104. A. Pathak, R. Srihari, BREAKING! presenting fake news corpus for automated fact checking, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (Florence, Italy) (Association for Computational Linguistics, Stroudsburg, 2019), pp. 357–362

    Google Scholar 

  105. F.K.A. Salem, R. Al Feel, S. Elbassuoni, M. Jaber, M. Farah, Fakes: a fake news dataset around the Syrian war, in Proceedings of the International AAAI Conference on Web and Social Media, vol. 13 (2019), pp. 573–582

    Google Scholar 

  106. F. Torabi Asr, M. Taboada, Big data and quality data for fake news and misinformation detection. Big Data Soc. 6(1) (2019). https://doi.org/10.1177/2053951719843310

  107. N. Abokhodair, D. Yoo, D.W. McDonald, Dissecting a social botnet: growth, content and influence in twitter, in Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing (ACM, New York, 2015), pp. 839–851

    Google Scholar 

  108. O. Varol, E. Ferrara, C.A. Davis, F. Menczer, A. Flammini, Online human-bot interactions: detection, estimation, and characterization, in Eleventh International AAAI Conference on Web and Social Media (2017)

    Google Scholar 

  109. R.J. Oentaryo, A. Murdopo, P.K. Prasetyo, E.-P. Lim, On profiling bots in social media, in International Conference on Social Informatics (Springer, Berlin, 2016), pp. 92–109

    Google Scholar 

  110. N. Agarwal, S. Jabin, S.Z. Hussain, et al., Analyzing real and fake users in Facebook network based on emotions, in 2019 11th International Conference on Communication Systems and Networks (COMSNETS) (IEEE, Piscataway, 2019), pp. 110–117

    Google Scholar 

  111. R. Plutchik, Emotions: a general psychoevolutionary theory. Approaches Emotion 1984, 197–219 (1984)

    Google Scholar 

  112. J. Echeverrìa, E. De Cristofaro, N. Kourtellis, I. Leontiadis, G. Stringhini, S. Zhou, Lobo: evaluation of generalization deficiencies in twitter bot classifiers, in The 34th Annual Computer Security Applications Conference (ACSAC’18) (ACM, 2018), pp. 137–146

    Google Scholar 

  113. N. Chavoshi, H. Hamooni, A. Mueen, Identifying correlated bots in twitter, in International Conference on Social Informatics (Springer, Berlin, 2016), pp. 14–21

    Google Scholar 

  114. A. Anwar, U. Yaqub, Bot detection in twitter landscape using unsupervised learning, in The 21st Annual International Conference on Digital Government Research (2020), pp. 329–330

    Google Scholar 

  115. C.A. Davis, O. Varol, E. Ferrara, A. Flammini, F. Menczer, Botornot: a system to evaluate social bots, in Proceedings of the 25th International Conference Companion on World Wide Web, IW3C2 (2016), pp. 273–274

    Google Scholar 

  116. N. Chavoshi, H. Hamooni, A. Mueen, On-demand bot detection and archival system, in Proceedings of the 26th International Conference on World Wide Web Companion, IW3C2 (2017), pp. 183–187

    Google Scholar 

  117. V. Subrahmanian, A. Azaria, S. Durst, V. Kagan, A. Galstyan, K. Lerman, L. Zhu, E. Ferrara, A. Flammini, F. Menczer, The DARPA twitter bot challenge. Computer 49(6), 38–46 (2016)

    Article  Google Scholar 

  118. F. Rangel, P. Rosso, Overview of the 7th author profiling task at PAN 2019: bots and gender profiling in twitter, in Proceedings of the CEUR Workshop, Lugano, Switzerland (2019), pp. 1–36

    Google Scholar 

  119. DFRLab, #botspot: twelve ways to spot a bot. https://medium.com/dfrlab/botspot-twelve-ways-to-spot-a-bot-aedc7d9c110c. Last checked August 2020

  120. M. Conti, R. Poovendran, M. Secchiero, Fakebook: detecting fake profiles in on-line social networks, in Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) (IEEE Computer Society, Washington, 2012), pp. 1071–1078

    Google Scholar 

  121. Q. Cao, M. Sirivianos, X. Yang, T. Pregueiro, Aiding the detection of fake accounts in large scale social online services, in Proceedings of the Ninth USENIX conference on Networked Systems Design and Implementation (USENIX Association, Berkeley, 2012), p. 15

    Google Scholar 

  122. M. La Morgia, A. Mei, S. Raponi, J. Stefa, Time-zone geolocation of crowds in the dark web,” in 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (IEEE, Piscataway, 2018), pp. 445–455

    Google Scholar 

  123. M. La Morgia, A. Mei, E. Nemmi, S. Raponi, J. Stefa, Nationality and geolocation-based profiling in the dark (web). IEEE Trans. Serv. Comput. (2019)

    Google Scholar 

  124. S. Gurajala, J.S. White, B. Hudson, J.N. Matthews, Fake twitter accounts: profile characteristics obtained using an activity-based pattern detection approach, in Proceedings of the 2015 International Conference on Social Media and Society (ACM, New York, 2015), p. 9

    Google Scholar 

  125. D. Ramalingam, V. Chinnaiah, Fake profile detection techniques in large-scale online social networks: a comprehensive review. Comput. Electr. Eng. 65, 165–177 (2018)

    Article  Google Scholar 

  126. S. Adikari, K. Dutta, Identifying fake profiles in linkedin, in PACIS (2014), p. 278

    Google Scholar 

  127. J. Haikarainen, Astroturfing as a global phenomenon (2014)

    Google Scholar 

  128. F.B. Keller, D. Schoch, S. Stier, J. Yang, Political astroturfing on twitter: how to coordinate a disinformation campaign. Polit. Commun. 37(2), 256–280 (2020)

    Article  Google Scholar 

  129. J. Zhang, D. Carpenter, M. Ko, Online astroturfing: a theoretical perspective (2013)

    Google Scholar 

  130. T. Chen, N.H. Alallaq, W. Niu, Y. Wang, X. Bai, J. Liu, Y. Xiang, T. Wu, J. Liu, A hidden astroturfing detection approach base on emotion analysis, in International Conference on Knowledge Science, Engineering and Management (Springer, Berlin, 2017), pp. 55–66

    Google Scholar 

  131. S. Mahbub, E. Pardede, A. Kayes, W. Rahayu, Controlling astroturfing on the internet: a survey on detection techniques and research challenges. Int. J. Web Grid Serv. 15(2), 139–158 (2019)

    Article  Google Scholar 

  132. A.H. Wang, Detecting spam bots in online social networking sites: a machine learning approach, in IFIP Annual Conference on Data and Applications Security and Privacy (Springer, Berlin, 2010), pp. 335–342

    Google Scholar 

  133. G. Stringhini, C. Kruegel, G. Vigna, Detecting spammers on social networks, in Proceedings of the 26th Annual Computer Security Applications Conference (ACM, New York, 2010), pp. 1–9

    Google Scholar 

  134. M. Singh, D. Bansal, S. Sofat, Who is who on twitter–spammer, fake or compromised account? A tool to reveal true identity in real-time. Cybern. Syst. 49(1), 1–25 (2018)

    Google Scholar 

  135. Z. Miller, B. Dickinson, W. Deitrick, W. Hu, A.H. Wang, Twitter spammer detection using data stream clustering. Inf. Sci. 260, 64–73 (2014)

    Article  Google Scholar 

  136. C. Yang, R. C. Harkreader, G. Gu, Die free or live hard? Empirical evaluation and new design for fighting evolving twitter spammers, in International Workshop on Recent Advances in Intrusion Detection (Springer, Berlin, 2011), pp. 318–337

    Google Scholar 

  137. C. Grier, K. Thomas, V. Paxson, M. Zhang, @ spam: the underground on 140 characters or less, in Proceedings of the 17th ACM conference on Computer and Communications Security (ACM, Berlin, 2010), pp. 27–37

    Google Scholar 

  138. K. Thomas, C. Grier, D. Song, V. Paxson, Suspended accounts in retrospect: an analysis of twitter spam, in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference (ACM, New York, 2011), pp. 243–258

    Book  Google Scholar 

  139. A. Caspi, P. Gorsky, Online deception: prevalence, motivation, and emotion. CyberPsychol. Behav. 9(1), 54–59 (2006)

    Article  Google Scholar 

  140. Z. Bu, Z. Xia, J. Wang, A sock puppet detection algorithm on virtual spaces. Knowl.-Based Syst. 37, 366–377 (2013)

    Article  Google Scholar 

  141. D. Liu, Q. Wu, W. Han, B. Zhou, Sockpuppet gang detection on social media sites. Front. Comput. Sci. 10(1), 124–135 (2016)

    Article  Google Scholar 

  142. S. Kumar, J. Cheng, J. Leskovec, V. Subrahmanian, An army of me: sockpuppets in online discussion communities, in Proceedings of the 26th International Conference on World Wide Web, IW3C2 (2017), pp. 857–866

    Google Scholar 

  143. T. Solorio, R. Hasan, M. Mizan, A case study of sockpuppet detection in wikipedia, in Proceedings of the Workshop on Language Analysis in Social Media (2013), pp. 59–68

    Google Scholar 

  144. M. Tsikerdekis, S. Zeadally, Multiple account identity deception detection in social media using nonverbal behavior. IEEE Trans. Inf. Forensics Secur. 9(8), 1311–1321 (2014)

    Article  Google Scholar 

  145. B. Stone, M. Richtel, The hand that controls the sock puppet could get slapped. N.Y. Times (2007)

    Google Scholar 

  146. R.M. Milner, Media lingua franca: fixity, novelty, and vernacular creativity in internet memes. AoIR Sel. Pap. Internet Res. 3 (2013)

    Google Scholar 

  147. L. Shifman, Memes in Digital Culture (MIT Press, Cambridge, 2014)

    Google Scholar 

  148. J. Leskovec, L. Backstrom, J. Kleinberg, Meme-tracking and the dynamics of the news cycle, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2009), pp. 497–506

    Google Scholar 

  149. J. Ratkiewicz, M. Conover, M. Meiss, B. Gonçalves, S. Patil, A. Flammini, F. Menczer, Truthy: mapping the spread of astroturf in microblog streams, in The 20th International Conference Companion on World Wide Web (WWW’11) (ACM, New York, 2011), pp. 249–252

    Google Scholar 

  150. C. Bauckhage, Insights into internet memes, in Fifth International AAAI Conference on Weblogs and Social Media (2011)

    Google Scholar 

  151. C. W. Seah, H. L. Chieu, K. M. A. Chai, L.-N. Teow, L. W. Yeong, Troll detection by domain-adapting sentiment analysis, in 2015 18th International Conference on Information Fusion (Fusion) (IEEE, Piscataway, 2015), pp. 792–799

    Google Scholar 

  152. P. Fornacciari, M. Mordonini, A. Poggi, L. Sani, M. Tomaiuolo, A holistic system for troll detection on twitter. Comput. Hum. Behav. 89, 258–268 (2018)

    Article  Google Scholar 

  153. F.J. Ortega, J.A. Troyano, F.L. Cruz, C.G. Vallejo, F. EnríQuez, Propagation of trust and distrust for the detection of trolls in a social network. Comput. Netw. 56(12), 2884–2895 (2012)

    Article  Google Scholar 

  154. E. Cambria, P. Chandra, A. Sharma, A. Hussain, Do not feel the trolls, in ISWC, Shanghai (2010)

    Google Scholar 

  155. P. Galán-García, J.G.D.L. Puerta, C.L. Gómez, I. Santos, P.G. Bringas, Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying. Logic J. IGPL 24(1), 42–53 (2016)

    MathSciNet  Google Scholar 

  156. J. Im, E. Chandrasekharan, J. Sargent, P. Lighthammer, T. Denby, A. Bhargava, L. Hemphill, D. Jurgens, E. Gilbert, Still out there: modeling and identifying Russian troll accounts on twitter (2019). arXiv preprint arXiv:1901.11162

    Google Scholar 

  157. D. Kim, T. Graham, Z. Wan, M.-A. Rizoiu, Analysing user identity via time-sensitive semantic edit distance (t-SED): a case study of Russian trolls on twitter. J. Commer. Soc. Sci. 2(2), 331–351 (2019)

    Google Scholar 

  158. T. Mihaylov, G. Georgiev, P. Nakov, Finding opinion manipulation trolls in news community forums, in Proceedings of the Nineteenth Conference on Computational Natural Language Learning (2015), pp. 310–314

    Google Scholar 

  159. T. Mihaylov, P. Nakov, Hunting for troll comments in news community forums, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2016), pp. 399–405

    Google Scholar 

  160. S. Cresci, M. Petrocchi, A. Spognardi, S. Tognazzi, From reaction to proaction: unexplored ways to the detection of evolving spambots, in Companion Proceedings of the Web Conference 2018 (WWW’18) (2018), pp. 1469–1470

    Google Scholar 

  161. D. Boneh, A.J. Grotto, P. McDaniel, N. Papernot, How relevant is the turing test in the age of sophisbots? IEEE Secur. Priv. 17(6), 64–71 (2019)

    Article  Google Scholar 

  162. S. Raponi, I. Ali, G. Oligeri, Sound of guns: digital forensics of gun audio samples meets artificial intelligence (2020). arXiv preprint arXiv:2004.07948

    Google Scholar 

  163. Y. Li, M.-C. Chang, S. Lyu, In ictu oculi: exposing ai generated fake face videos by detecting eye blinking (2018). arXiv preprint arXiv:1806.02877

    Google Scholar 

  164. A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, M. Nießner, Faceforensics: a large-scale video dataset for forgery detection in human faces (2018). arXiv preprint arXiv:1803.09179

    Google Scholar 

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  1. Hamad Bin Khalifa University, College of Science & Engineering, Education City, Doha, Qatar

    Roberto Di Pietro, Simone Raponi & Maurantonio Caprolu

  2. National Research Council, Institute of Informatics and Telematics, Pisa, Italy

    Stefano Cresci

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Di Pietro, R., Raponi, S., Caprolu, M., Cresci, S. (2021). Information Disorder. In: New Dimensions of Information Warfare. Advances in Information Security, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-60618-3_2

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