Advertisement

Is It Really Fake? – Towards an Understanding of Fake News in Social Media Communication

  • Judith Meinert
  • Milad Mirbabaie
  • Sebastian Dungs
  • Ahmet Aker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10913)

Abstract

This paper outlines the development of Fake News and seeks to clarify different perspectives regarding the term within Social Media communication. Current information systems, such as Social Media platforms, allow real-time communication, enabling people to produce and spread false information and rumors within a few seconds, potentially reaching a wide audience. This, in turn, could have negative impacts on politics, society, and business. To demystify Fake News and create a common understanding, we analyzed the literature on Fake News and summarized existing articles as well as strategies tested to detect Fake News. We conclude that detection methods mostly perform binary classifications based on linguistic features without providing explanations or further information to the user.

Keywords

Fake news Fake news detection Social media Social media analysis Social media analytics 

Notes

Acknowledgements

This work is supported by the German Research Foundation (DFG) under grant No. GRK 2167, Research Training Group “User-Centred Social Media”. We also thank our student assistant Annika Deubel for supporting us with the literature review.

References

  1. 1.
    Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–236 (2017).  https://doi.org/10.3386/w23089CrossRefGoogle Scholar
  2. 2.
    Berghel, H.: Lies, damn lies, and fake news. Computer 50(2), 80–85 (2017).  https://doi.org/10.1109/MC.2017.56CrossRefGoogle Scholar
  3. 3.
    Isaac, M.: Facebook, in cross hairs after election, is said to question its influence. The New York Times (2016). https://www.nytimes.com/2016/11/14/technology/facebook-is-said-to-question-its-influence-in-election.html. Accessed 31 Jan 2018
  4. 4.
    Ott, B.L.: The age of Twitter: Donald J. Trump and the politics of debasement. Crit. Stud. Media Commun. 34(1), 59–68 (2017).  https://doi.org/10.1080/15295036.2016.1266686CrossRefGoogle Scholar
  5. 5.
    Rogers, K., Bromwich, J.E.: The Hoaxes, Fake News, and Misinformation We Saw on Election Day. The New York Times (2016). https://www.nytimes.com/2016/11/09/us/politics/debunk-fake-news-election-day.html. Accessed 6 Feb 2018
  6. 6.
    Pennycook, G., Cannon, T.D., Rand, D.G.: Prior Exposure Increases Perceived Accuracy of Fake News. Social Science Research Network (2017). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2958246. Accessed 6 Feb 2018
  7. 7.
    Howard, P.N., Bolsover, G., Kollanyi, B., Bradshaw, S., Neudert, L.M.: Junk news and bots during the US election: What were Michigan voters sharing over Twitter. Data Memo, January 2017. Project on Computational Propaganda, Oxford (2017). http://comprop.oii.ox.ac.uk/2017/03/26/junk-news-and-bots-during-the-uselection-what-were-michigan-voters-sharing-over-twitter. Accessed 6 Feb 2018
  8. 8.
    Mitchell, A., Gottfried, J., Matsa, K.E.: Millennials and political news. Pew Research Center (2015). http://www.journalism.org/2015/06/01/millennials-political-news/. Accessed 6 Feb 2018
  9. 9.
    Stieglitz, S., Mirbabaie, M., Ross, B., Neuberger, C.: Social media analytics-challenges in topic discovery, data collection, and data preparation. Int. J. Inf. Manag. 39, 156–168 (2018).  https://doi.org/10.1016/j.ijinfomgt.2017.12.002CrossRefGoogle Scholar
  10. 10.
    Starbird, K.: Examining the alternative media ecosystem through the production of alternative narratives of mass shooting events on Twitter. In: ICWSM, pp. 230–239 (2017)Google Scholar
  11. 11.
    Gallacher, J.D., Kaminska, M., Kollanyi, B., Yasseri, T., Howard, P.N.: Social Media and News Sources during the 2017 UK General Election. Data Memo, June 2017. Project on Computational Propaganda, Oxford (2017). http://comprop.oii.ox.ac.uk/wp-content/uploads/sites/89/2017/06/Social-Media-and-News-Sources-during-the-2017-UK-General-Election.pdf. Accessed 6 Feb 2018
  12. 12.
    Connolly, K., Chrisafis, A., McPherson, P., Kirchgaessner, S., Haas, B., Phillips, D., Hunt, E., Safi, M.: Fake news: an insidious trend that’s fast becoming a global problem. The Guardian (2016). https://www.theguardian.com/media/2016/dec/02/fake-news-facebook-us-election-around-the-world. Accessed 31 Jan 2018
  13. 13.
    Gabriel, R., Röhrs, H.-P.: Trends, Chancen und Risiken von Social-Media-Anwendungen – eine kritische Betrachtung. In: Gabriel, R., Röhrs, H.-P. (eds.) Social Media, pp. 219–243. Springer, Heidelberg (2017).  https://doi.org/10.1007/978-3-662-53991-0_9CrossRefGoogle Scholar
  14. 14.
    Goodman, E.: How has media policy responded to fake news? Media Policy Blog (2017). http://blogs.lse.ac.uk/mediapolicyproject/2017/02/07/how-has-media-policy-responded-to-fake-news/. Accessed 31 Jan 2018
  15. 15.
    Mirbabaie, M., Ehnis, C., Stieglitz, S., Bunker, D.: Communication roles in public events. In: Doolin, B., Lamprou, E., Mitev, N., McLeod, L. (eds.) Working Conference on Information Systems and Organizations, pp. 207–218. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45708-5_13CrossRefGoogle Scholar
  16. 16.
    Mirbabaie, M., Zapatka, E.: Sensemaking in social media crisis communication - a case study on the Brussels bombings in 2016. In: Proceedings of the Twenty-Fifth European Conference on Information Systems (ECIS) (2017). https://aisel.aisnet.org/ecis2017_rp/138/. Accessed 6 Feb 2018
  17. 17.
    BMJV Aktuelle Gesetzgebungsverfahren. Gesetz zur Verbesserung der Rechtsdurchsetzung in sozialen Netzwerken (Netzwerkdurchsetzungsgesetz – NetzDG) (2017). https://www.bmjv.de/SharedDocs/Gesetzgebungsverfahren/Dokumente/BGBl_NetzDG.html;jsessnid=111BF7BB5DA1C6A0A4D6F8912345D764.1_cid324?nn=6712350. Accessed 8 Feb 2018
  18. 18.
    Ciampaglia, G.L., Shiralkar, P., Rocha, L.M., Bollen, J., Menczer, F., Flammini, A.: Computational fact checking from knowledge networks. PLoS ONE 10(6), e0128193 (2015).  https://doi.org/10.1371/journal.pone.0128193CrossRefGoogle Scholar
  19. 19.
    Douglas, K., Ang, C.S., Deravi, F.: Farewell to truth? Conspiracy theories and fake news on social media. Psychologist 30, 36–42 (2017)Google Scholar
  20. 20.
    Klein, D.O., Wueller, J.R.: Fake news: a legal perspective. J. Internet Law 20(10), 6–13 (2017)Google Scholar
  21. 21.
    Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R.: Detection and resolution of rumours in social media: a survey. ACM Comput. Surv. (2017). https://arxiv.org/pdf/1704.00656.pdf. Accessed 6 Feb 2018
  22. 22.
    Starbird, K., Spiro, E., Edwards, I., Zhou, K., Maddock, J., Narasimhan, S.: Could this be true? I think so! Expressed uncertainty in online rumoring. In: CHI 2016 Proceedings of the 2016 CHI Conference on Human factors in Computing Systems, pp. 360–371. ACM (2016).  https://doi.org/10.1145/2858036.2858551
  23. 23.
    Oh, O., Agrawal, M., Rao, H.R.: Community intelligence and social media services: a rumor theoretic analysis of tweets during social crises. MIS Q. 37(2), 407–426 (2013)CrossRefGoogle Scholar
  24. 24.
    Stieglitz, S., Bunker, D., Mirbabaie, M., Ehnis, C.: Sense-making in social media during extreme events. J. Conting. Crisis Manag. 26, 1–12 (2017).  https://doi.org/10.1111/1468-5973.12193CrossRefGoogle Scholar
  25. 25.
    Stieglitz, S., Mirbabaie, M., Milde, M.: Social positions and collective sense-making in crisis communication. Int. J. Hum.-Comput. Interact. (2018).  https://doi.org/10.1080/10447318.2018.1427830CrossRefGoogle Scholar
  26. 26.
    Hardalov, M., Koychev, I., Nakov, P.: In search of credible news. In: Dichev, C., Agre, G. (eds.) International Conference on Artificial Intelligence: Methodology, Systems, and Applications, pp. 172–180. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-44748-3_17CrossRefGoogle Scholar
  27. 27.
    Jack, C.: Lexicon of Lies: Terms for Problematic Information. Data and Society Research Institute, New York (2017). https://datasociety.net/pubs/oh/DataAndSociety_LexiconofLies.pdf. Accessed 6 Feb 2018
  28. 28.
    Silverman, C.: This Analysis Shows How Viral Fake Election News Stories Outperformed Real News on Facebook. BuzzFeed News (2016). https://www.buzzfeed.com/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook?utm_term=.oopAlP795#.wtEYJ9gba. Accessed 31 Jan 2018
  29. 29.
    Rubin, V., Conroy, N., Chen, Y., Cornwell, S.: Fake news or truth? using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pp. 7–17 (2016)Google Scholar
  30. 30.
    McClain, C.R.: Practices and promises of Facebook for science outreach: becoming a “Nerd of Trust”. PLoS Biol. 15(6), e2002020 (2017).  https://doi.org/10.1371/journal.pbio.2002020CrossRefGoogle Scholar
  31. 31.
    Conroy, N.J., Rubin, V.L., Chen, Y.: Automatic deception detection: methods for finding fake news. Proc. Assoc. Inf. Sci. Tech. 52(1), 1–4 (2015).  https://doi.org/10.1002/pra2.2015.145052010082CrossRefGoogle Scholar
  32. 32.
    Gil de Zúñiga, H., Molyneux, L., Zheng, P.: Social media, political expression, and political participation: panel analysis of lagged and concurrent relationships. J. Commun. 64(4), 612–634 (2014).  https://doi.org/10.1111/jcom.12103CrossRefGoogle Scholar
  33. 33.
    Stieglitz, S., Brockmann, T., Dang-Xuan, L.: Usage of social media for political communication. In: Proceedings of 16th Pacific Asia Conference on Information Systems, Ho Chi Minh City, Vietnam (2012)Google Scholar
  34. 34.
    Jong, W., Dückers, M.L.A.: Self-correcting mechanisms and echo-effects in social media: an analysis of the “gunman in the newsroom” crisis. Comput. Hum. Behav. 59, 334–341 (2016).  https://doi.org/10.1016/j.chb.2016.02.032CrossRefGoogle Scholar
  35. 35.
    Vargo, C.J., Guo, L., Amazeen, M.A.: The agenda-setting power of fake news: a big data analysis of the online media landscape from 2014 to 2016. New Media Soc. 10(6), 1–22 (2017).  https://doi.org/10.1177/1461444817712086CrossRefGoogle Scholar
  36. 36.
    Gerhart, N., Torres, R., Negahban, A.: Combatting fake news: an investigation of individuals’ information verification behaviors on social networking sites. In: Twenty-Third Americas Conference on Information Systems, Boston (2017)Google Scholar
  37. 37.
    Day, A., Thompson, E.: Live from New York, it’s the fake news! Saturday night live and the (Non)politics of parody. Pop. Commun. 10(1–2), 170–182 (2012).  https://doi.org/10.1080/15405702.2012.638582CrossRefGoogle Scholar
  38. 38.
    Broussard, P.L.: Fake news, real hip: rhetorical dimensions of ironic communication in mass media. Unpublished thesis, The University of Tennessee at Chattanooga, Tennessee (2013)Google Scholar
  39. 39.
    Lazer, D., Baum, M., Grinberg, N., Friedland, L., Joseph, K., Hobbs, W., Mattsson, C.: Combating Fake News: An Agenda for Research and Action (2017). http://www.sipotra.it/wp-content/uploads/2017/06/Combating-Fake-News.pdf. Accessed 31 Jan 2018
  40. 40.
    Elyashar, A., Bendahan, J., Puzis, R.: Is the Online Discussion Manipulated? Quantifying the Online Discussion Authenticity within Online Social Media. arXiv preprint arXiv:1708.02763 (2017)
  41. 41.
    Lang, A.: The limited capacity model of mediated message processing. J. Commun. 50(1), 46–70 (2000).  https://doi.org/10.1111/j.1460-2466.2000.tb02833.xCrossRefGoogle Scholar
  42. 42.
    Nyhan, B., Reifler, J.: Displacing misinformation about events: an experimental test of causal corrections. J. Exp. Polit. Sci. 2(1), 81–93 (2015).  https://doi.org/10.1017/XPS.2014.22CrossRefGoogle Scholar
  43. 43.
    Metzger, M.J., Flanagin, A.J., Medders, R.B.: Social and heuristic approaches to credibility evaluation online. J. Commun. 60(3), 413–439 (2010).  https://doi.org/10.1111/j.1460-2466.2010.01488.xCrossRefGoogle Scholar
  44. 44.
    Sundar, S.S.: The MAIN model: a heuristic approach to understanding technology effects on credibility. In: Metzger, M., Flanagin, A. (eds.) Digital Media, Youth, and Credibility, pp. 73–100. MIT Press, Cambridge (2008)Google Scholar
  45. 45.
    Sundar, S.S., Oeldorf-Hirsch, A., Xu, Q.: The bandwagon effect of collaborative filtering technology. In CHI 2008 Extended Abstracts on Human Factors in Computing Systems, pp. 3453–3458. ACM (2008).  https://doi.org/10.1145/1358628.1358873
  46. 46.
    Festinger, L.: A Theory of Cognitive Dissonance, vol. 2. Stanford University Press, Palo Alto (1962)Google Scholar
  47. 47.
    Tan, E.E.G., Ang, B.: Clickbait: Fake News and Role of the State. RSIS Commentaries, 026-17 (2017)Google Scholar
  48. 48.
    Gross, M.: The dangers of a post-truth world. Curr. Biol. 27(1), R1–R4 (2017).  https://doi.org/10.1016/j.cub.2016.12.034CrossRefGoogle Scholar
  49. 49.
    Jin, Z., Cao, J., Zhang, Y., Zhou, J., Tian, Q.: Novel visual and statistical image features for microblogs news verification. IEEE Trans. Multimed. 19(3), 598–608 (2017).  https://doi.org/10.1109/TMM.2016.2617078CrossRefGoogle Scholar
  50. 50.
    Markowitz, D.M., Hancock, J.T.: Linguistic traces of a scientific fraud: the case of Diederik Stapel. PLoS ONE 9(8), e105937 (2014).  https://doi.org/10.1371/journal.pone.0105937CrossRefGoogle Scholar
  51. 51.
    Lendvai, P., Reichel, U.D.: Contradiction Detection for Rumorous Claims. arXiv preprint arXiv:1611.02588 (2016)
  52. 52.
    Ma, J., Gao, W., Wei, Z., Lu, Y., Wong, K.F.: Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1751–1754. ACM, New York (2015).  https://doi.org/10.1145/2806416.2806607
  53. 53.
    Jin, Z., Cao, J., Zhang, Y., Luo, J.: News verification by exploiting conflicting social viewpoints in microblogs. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016) (2016). https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12128/12049. Accessed 6 Feb 2018
  54. 54.
    Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)CrossRefGoogle Scholar
  55. 55.
    Appelman, A., Sundar, S.S.: Measuring message credibility: construction and validation of an exclusive scale. Journal. Mass Commun. Q. 93(1), 59–79 (2016).  https://doi.org/10.1177/1077699015606057CrossRefGoogle Scholar
  56. 56.
    Tufekci, Z.: Mark Zuckerberg is in Denial. The New York Times (2016). https://www.nytimes.com/2016/11/15/opinion/mark-zuckerberg-is-in-denial.html. Accessed 31 Jan 2018
  57. 57.
    Tintarev, N., Masthoff, J.: Evaluating the effectiveness of explanations for recommender systems. User Model. User-Adap. Interact. 22(4–5), 399–439 (2012).  https://doi.org/10.1007/s11257-011-9117-5CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Judith Meinert
    • 1
  • Milad Mirbabaie
    • 1
  • Sebastian Dungs
    • 1
  • Ahmet Aker
    • 1
  1. 1.University of Duisburg-EssenDuisburgGermany

Personalised recommendations