skip to main content
10.1145/3400806.3400817acmotherconferencesArticle/Chapter ViewAbstractPublication PagessmsocietyConference Proceedingsconference-collections
research-article

Coordinated Link Sharing Behavior as a Signal to Surface Sources of Problematic Information on Facebook

Published:22 July 2020Publication History

ABSTRACT

Despite widespread concern over the role played by disinformation during recent electoral processes, the intrinsic elusiveness of the subject hinders efforts aimed at estimating its prevalence and effect. While there has been proliferation of attempts to define, understand and fight the spread of problematic information in contemporary media ecosystems, most of these attempts focus on detecting false content and/or bad actors. For instance, several existing studies rely on lists of problematic content or news media sources compiled by fact-checkers. However, these lists may quickly become obsolete leading to unreliable estimates. Using media manipulation as a frame, along with a revised version of the “coordinated inauthentic behavior” original definition, in this paper, we argue for a wider ecological focus. Leveraging a method designed to detect “coordinated links sharing behavior” (CLSB), we introduce and assess an approach aimed at creating and keeping lists of potentially problematic sources updated by analyzing the URLs shared on Facebook by public groups, pages, and verified profiles. We show how CLSB is consistently associated with higher risks of encountering problematic news sources across three different datasets of news stories and can be thus used as a signal to support manual and automatic detection of problematic information.

References

  1. Hunt Allcott, Matthew Gentzkow, and Chuan Yu. 2019. Trends in the diffusion of misinformation on social media. Research & Politics 6, 2 (April 2019), 2053168019848554. DOI: https://doi.org/10.1177/2053168019848554Google ScholarGoogle ScholarCross RefCross Ref
  2. Marco Bastos and Johan Farkas. 2019. “Donald Trump Is My President!”: The Internet Research Agency Propaganda Machine. Social Media + Society 5, 3 (July 2019), 2056305119865466. DOI: https://doi.org/10.1177/2056305119865466Google ScholarGoogle Scholar
  3. Marco Bastos and Dan Mercea. 2018. Parametrizing Brexit: mapping Twitter political space to parliamentary constituencies. Inf. Commun. Soc. 21, 7 (July 2018), 921–939. DOI: https://doi.org/10.1080/1369118X.2018.1433224Google ScholarGoogle ScholarCross RefCross Ref
  4. Marco Bastos and Dan Mercea. 2019. The Brexit Botnet and User-Generated Hyperpartisan News. Soc. Sci. Comput. Rev. 37, 1 (February 2019), 38–54. DOI: https://doi.org/10.1177/0894439317734157Google ScholarGoogle Scholar
  5. Marco T. Bastos. 2019. This Account Doesn't Exist: Tweet Decay and the Politics of Deletion in the Brexit Debate. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3485789Google ScholarGoogle Scholar
  6. Yochai Benkler. 2019. Cautionary Notes on Disinformation and the Origins of Distrust. Retrieved from https://mediawell.ssrc.org/expert-reflections/cautionary-notes-on-disinformation-benkler/Google ScholarGoogle Scholar
  7. Alessandro Bessi and Emilio Ferrara. 2016. Social Bots Distort the 2016 US Presidential Election Online Discussion. First Monday. Retrieved from https://papers.ssrn.com/abstract=2982233Google ScholarGoogle Scholar
  8. Axel Bruns, Tim Highfield, and Jean Burgess. 2014. The Arab Spring and social media audiences: English and Arabic Twitter users and their networks. American behavioral scientist, 57(7), 871-898. DOI: https://doi.org/10.1177/0002764213479374Google ScholarGoogle Scholar
  9. Gabriella Coleman. 2015. Hacker, hoaxer, whistleblower, spy: The many faces of Anonymous. Verso books, New York, NY.Google ScholarGoogle Scholar
  10. Jessie Daniels. 2009. Cloaked websites: propaganda, cyber-racism and epistemology in the digital era. New Media & Society 11, 5 (August 2009), 659–683. DOI: https://doi.org/10.1177/1461444809105345Google ScholarGoogle ScholarCross RefCross Ref
  11. Jessie Daniels. 2014. From crisis pregnancy centers to teenbreaks. com: anti-abortion activism's use of cloaked websites. In Cyberactivism on the participatory web. Routledge, 152–166. Retrieved from https://www.taylorfrancis.com/books/e/9781315885797/chapters/10.4324/978131 5885797-12Google ScholarGoogle Scholar
  12. Joan Donovan and Brian Friedberg. 2019. Source hacking: Media manipulation in practice. Data&Society. Retrieved from https://datasociety. net/output/source-hacking-media-manipulation-in-practiceGoogle ScholarGoogle Scholar
  13. Johan Farkas, Jannick Schou, and Christina Neumayer. 2018. Cloaked Facebook pages: Exploring fake Islamist propaganda in social media. New Media & Society 20, 5 (May2018), 1850–1867. DOI: https://doi.org/10.1177/1461444817707759Google ScholarGoogle ScholarCross RefCross Ref
  14. Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016. The rise of social bots. Communications of the ACM, 59(7), 96-104.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Richard Fletcher and Rasmus Kleis Nielsen. 2017. People dont trust news media and this is key to the global misinformation debate. AA. VV. , Understanding and Addressing the Disinformation Ecosystem (2017), 13–17. Retrieved from https://firstdraftnews.org/latest/understanding-disinformation/Google ScholarGoogle Scholar
  16. Deen Freelon, Charlton McIlwain, and Meredith Clark. 2018. Quantifying the power and consequences of social media protest. New Media & Society 20, 3 (March 2018), 990–1011. DOI: https://doi.org/10.1177/1461444816676646Google ScholarGoogle ScholarCross RefCross Ref
  17. Fabio Giglietto, Nicola Righetti, Luca Rossi, and Giada Marino. 2020. It takes a village to manipulate the media: coordinated link sharing behavior during 2018 and 2019 Italian elections. Information, Communication & Society, 1-25.Google ScholarGoogle Scholar
  18. Fabio Giglietto, Nicola Righetti, and Luca Rossi. 2020. CooRnet: Detect coordinated link sharing behavior on social media. R package version 0.9.0. https://github.com/fabiogiglietto/CooRnetGoogle ScholarGoogle Scholar
  19. Fabio Giglietto, Nicola Righetti, and Giada Marino. 2019. Understanding Coordinated and Inauthentic Link Sharing Behavior on Facebook in the Run-up to 2018 General Election and 2019 European Election in Italy. DOI: https://doi.org/10.31235/osf.io/3jtehGoogle ScholarGoogle Scholar
  20. Nathaniel Gleicher. 2018. Coordinated Inauthentic Behavior Explained. Retrieved from https://about.fb.com/news/2018/12/inside-feed-coordinated-inauthentic-behavior/Google ScholarGoogle Scholar
  21. Nir Grinberg, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, and David Lazer. 2019. Fake news on Twitter during the 2016 U.S. presidential election. Science 363, 6425 (January 2019), 374–378. DOI: https://doi.org/10.1126/science.aau2706Google ScholarGoogle ScholarCross RefCross Ref
  22. Andrew Guess, Jonathan Nagler, and Joshua Tucker. 2019. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Sci Adv 5, 1 (January 2019), eaau4586. DOI: https://doi.org/10.1126/sciadv.aau4586Google ScholarGoogle ScholarCross RefCross Ref
  23. Andrew Guess, Brendan Nyhan, and Jason Reifler. 2018. Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign. European Research Council 9, (2018). Retrieved from https://pdfs.semanticscholar.org/a795/b451b3d38ca1d22a6075dbb0be4fc94b4000.pdfGoogle ScholarGoogle Scholar
  24. Lei Guo and Chris Vargo. 2018. “Fake News” and Emerging Online Media Ecosystem: An Integrated Intermedia Agenda-Setting Analysis of the 2016 U.S. Presidential Election. Communic. Res. (June 2018), 0093650218777177. DOI: https://doi.org/10.1177/0093650218777177Google ScholarGoogle Scholar
  25. Zied Ben Houidi, Giuseppe Scavo, Stefano Traverso, Renata Teixeira, Marco Mellia, and Soumen Ganguly. 2019. The News We Like Are Not the News We Visit: News Categories Popularity in Usage Data. In Proceedings of the International AAAI Conference on Web and Social Media, aaai.org, 91–102.Google ScholarGoogle Scholar
  26. Philip N. Howard, Samuel Woolley, and Ryan Calo. 2018. Algorithms, bots, and political communication in the US 2016 election: The challenge of automated political communication for election law and administration. Journal of Information Technology & Politics 15, 2 (April 2018), 81–93. DOI: https://doi.org/10.1080/19331681.2018.1448735Google ScholarGoogle ScholarCross RefCross Ref
  27. Caroline Jack. 2017. Lexicon of lies: Terms for problematic information. Data & Society 3, (2017). Retrieved from https://apo.org.au/sites/default/files/resource-files/2017/08/apo-nid183786-11805 16.pdfGoogle ScholarGoogle Scholar
  28. Henry Jenkins. 2006. Fans, Bloggers, and Gamers: Exploring Participatory Culture. NYU Press, New York, NY.Google ScholarGoogle Scholar
  29. Franziska B. Keller, David Schoch, Sebastian Stier, and Junghwan Yang. 2019. Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign. Political Communication (October 2019), 1–25. DOI: https://doi.org/10.1080/10584609.2019.1661888Google ScholarGoogle Scholar
  30. Lance W. Bennett and Alexandra Segerberg. 2013. The Logic of Connective Action: Digital Media and the Personalization of Contentious Politics. Information, Communication & Society, 15:5, 739-768. DOI: https://doi.org/10.1080/1369118X.2012.670661Google ScholarGoogle ScholarCross RefCross Ref
  31. David Lazer, Matthew Baum, Yochai Benkler, Adam J. Berinsky, Kelly M. Greenhill, Filippo Menczer, Miriam J. Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, Michael Schudson, Steven A. Sloman, Cass R. Sunstein, Emily A. Thorson, Duncan J. Watts, and Jonathan L. Zittrain. 2018. The science of fake news. Science, 359(6380), 1094-1096.Google ScholarGoogle ScholarCross RefCross Ref
  32. Brian D. Loader and Dan Mercea. 2011. Networking democracy? Social media innovations and participatory politics. Information, Communication & Society, 14(6), 757-769. DOI: https://doi.org/10.1080/1369118X.2011.592648Google ScholarGoogle ScholarCross RefCross Ref
  33. Luca Luceri, Ashok Deb, Silvia Giordano, and Emilio Ferrara. 2019. Evolution of bot and human behavior during elections. First Monday 24, 9 (August 2019). DOI: https://doi.org/10.5210/fm.v24i9.10213Google ScholarGoogle Scholar
  34. Tessa Lyons. 2018. Increasing Our Efforts to Fight False News. Retrieved from https://about.fb.com/news/2018/06/increasing-our-efforts-to-fight-false-news/Google ScholarGoogle Scholar
  35. Marwick, A. E. (2018). Why do people share fake news? A sociotechnical model of media effects. Georgetown Law Technology Review, 2(2), 474-512.Google ScholarGoogle Scholar
  36. Alice Marwick and Rebecca Lewis. 2017. Media manipulation and disinformation online. New York: Data & Society Research Institute. Retrieved from https://apo.org.au/sites/default/files/resource-files/2017/05/apo-nid135936-12178 06.pdfGoogle ScholarGoogle Scholar
  37. Katerina Eva Matsa and Elisa Shearer. 2018. News use across social media platforms 2018. Pew Research Center. Retrieved from https://www.journalism.org/2018/09/10/news-use-across-social-media-platforms-2018/Google ScholarGoogle Scholar
  38. Fred Morstatter, Liang Wu, Tahora H. Nazer, Kathleen M. Carley, and Huan Liu. 2016. A new approach to bot detection: Striking the balance between precision and recall. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), ieeexplore.ieee.org, 533–540. DOI: https://doi.org/10.1109/ASONAM.2016.7752287Google ScholarGoogle ScholarCross RefCross Ref
  39. Jacob L. Nelson and Harsh Taneja. 2018. The small, disloyal fake news audience: The role of audience availability in fake news consumption. New Media & Society 20, 10 (October 2018), 3720–3737. DOI: https://doi.org/10.1177/1461444818758715Google ScholarGoogle ScholarCross RefCross Ref
  40. Dana Rotman, Sarah Vieweg, Sarita Yardi, Ed Chi, Jenny Preece, Ben Shneiderman, Peter Pirolli, and Tom Glaisyer. 2011. From slacktivism to activism: participatory culture in the age of social media. In CHI’11 Extended Abstracts on Human Factors in Computing Systems. dl.acm.org, 819–822. Retrieved from https://dl.acm.org/doi/abs/10.1145/1979742.1979543Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Meredith Salisbury and Jefferson Pooley. 2017. The #nofilter self: The contest for authenticity among social networking sites, 2002–2016. Soc. Sci. 6, 1 (2017), 10. Retrieved from https://www.mdpi.com/2076-0760/6/1/10Google ScholarGoogle ScholarCross RefCross Ref
  42. Giovanni C. Santia, Munif Ishad Mujib, and Jake Ryland Williams. 2019. Detecting Social Bots on Facebook in an Information Veracity Context. In Proceedings of the International AAAI Conference on Web and Social Media, wvvw.aaai.org, 463–472. Retrieved from https://wvvw.aaai.org/ojs/index.php/ICWSM/article/view/3244Google ScholarGoogle Scholar
  43. Clay Shirky. 2008. Here comes everybody: The power of organizing without organizations. Penguin Book, London, UK.Google ScholarGoogle Scholar
  44. Brian E. Weeks and Homero Gil de Zúñiga. 2019. What's Next? Six Observations for the Future of Political Misinformation Research. American Behavioral Scientist, 0002764219878236. DOI: https://doi.org/10.1177/0002764219878236Google ScholarGoogle Scholar
  45. Samuel C. Woolley and Philip N. Howard. 2016. Automation, algorithms, and politics| political communication, computational propaganda, and autonomous agents. International Journal of Communication, 10(2016), 4882–4890Google ScholarGoogle Scholar
  46. Kai-Cheng Yang, Onur Varol, Pik-Mai Hui, and Filippo Menczer. 2019. Scalable and Generalizable Social Bot Detection through Data Selection. arXiv [cs.CY]. Retrieved from http://arxiv.org/abs/1911.09179Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    SMSociety'20: International Conference on Social Media and Society
    July 2020
    317 pages
    ISBN:9781450376884
    DOI:10.1145/3400806

    Copyright © 2020 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 22 July 2020

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate78of189submissions,41%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format