Skip to main content

Human and Algorithmic Contributions to Misinformation Online - Identifying the Culprit

  • Conference paper
  • First Online:
Disinformation in Open Online Media (MISDOOM 2019)

Abstract

In times of massive fake news campaigns in social media, one may ask who is to blame for the spread of misinformation online. Are humans, in their limited capacity for rational self-reflection or responsible information use, guilty because they are the ones falling for the misinformation? Or are algorithms that provide the basis for filter bubble phenomena the cause of the rise of misinformation in particular in the political public discourse? In this paper, we look at both perspectives and see how both sides contribute to the problem of misinformation and how underlying metrics shape the problem.

This research was supported by the Digital Society research program funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

References

  1. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Technical report, National Bureau of Economic Research (2017)

    Google Scholar 

  2. Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., Stillwell, D.: Personality and patterns of facebook usage. In: Proceedings of the ACM Web Science Conference, pp. 36–44. ACM New York (2012)

    Google Scholar 

  3. Berger, J., Milkman, K.L.: What makes online content viral? J. Mark. Res. 49(2), 192–205 (2012)

    Article  Google Scholar 

  4. Bessi, A., Ferrara, E.: Social bots distort the 2016 us presidential election online discussion. First Monday 21(11-7) (2016)

    Google Scholar 

  5. Bouneffouf, D., Bouzeghoub, A., Ganarski, A.L.: Risk-aware recommender systems. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8226, pp. 57–65. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42054-2_8

    Chapter  Google Scholar 

  6. Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., Hauff, C.: Siren: A simulation framework for understanding the effects of recommender systems in online news environments. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 150–159. ACM (2019)

    Google Scholar 

  7. Bruns, S., Valdez, A.C., Greven, C., Ziefle, M., Schroeder, U.: What Should I read next? A personalized visual publication recommender system. In: Yamamoto, S. (ed.) HCI 2015. LNCS, vol. 9173, pp. 89–100. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20618-9_9

    Chapter  Google Scholar 

  8. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  9. Calero Valdez, A., Kluge, J., Ziefle, M.: Elitism, trust, opinion leadership and politics in social protests in germany. Energy Res. Soc. Sci. 43, 132–143 (2018)

    Article  Google Scholar 

  10. Calero Valdez, A., Ziefle, M., Verbert, K.: HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 123–126. ACM, New York (2016). https://doi.org/10.1145/2959100.2959158

  11. Childers, T.L.: Assessment of the psychometric properties of an opinion leadership scale. J. Mark. Res. 23, 184–188 (1986)

    Article  Google Scholar 

  12. Deffuant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Adv. Complex Syst. 3(01n04), 87–98 (2000)

    Article  Google Scholar 

  13. DeGroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc. 69(345), 118–121 (1974)

    Article  MATH  Google Scholar 

  14. Di Noia, T., Ostuni, V.C., Rosati, J., Tomeo, P., Di Sciascio, E.: An analysis of users’ propensity toward diversity in recommendations. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 285–288. ACM (2014)

    Google Scholar 

  15. Dimitrova, D.V., Shehata, A., Strömbäck, J., Nord, L.W.: The effects of digital media on political knowledge and participation in election campaigns: evidence from panel data. Commun. Res. 41(1), 95–118 (2014)

    Article  Google Scholar 

  16. Dretske, F.: Knowledge and the Flow of Information. MIT Press, Cambridge (1981)

    MATH  Google Scholar 

  17. Dylko, I., Dolgov, I., Hoffman, W., Eckhart, N., Molina, M., Aaziz, O.: The dark side of technology: an experimental investigation of the influence of customizability technology on online political selective exposure. Comput. Hum. Behav. 73, 181–190 (2017). https://doi.org/10.1016/j.chb.2017.03.031

    Article  Google Scholar 

  18. Fleder, D., Hosanagar, K.: Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Manag. Sci. 55(5), 697–712 (2009)

    Article  Google Scholar 

  19. Floridi, L.: Electronic Library. Brave. net. world: the internet as a disinformation superhighway? 14(6), 509–514 (1996)

    Google Scholar 

  20. Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333 (2014)

    Article  Google Scholar 

  21. Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 257–260. ACM (2010)

    Google Scholar 

  22. Gearhart, S., Zhang, W.: “Was it something i said?” “No, it was something you posted!” a study of the spiral of silence theory in social media contexts. Cyberpsychol. Behav. Soc. Netw. 18(4), 208–213 (2015)

    Article  Google Scholar 

  23. Gelman, A., Loken, E.: The garden of forking paths: why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Columbia University, Department of Statistics (2013)

    Google Scholar 

  24. Gerbner, G., Gross, L., Morgan, M., Signorielli, N., Shanahan, J.: Growing up with television: Cultivation processes. In: Media Effects: Advances in Theory and Research, vol. 2, pp. 43–67 (2002)

    Google Scholar 

  25. Gilbert, D.T., Brown, R.P., Pinel, E.C., Wilson, T.D.: The illusion of external agency. J. Pers. Soc. Psychol. 79(5), 690 (2000)

    Article  Google Scholar 

  26. Glynn, C.J., Huge, M.E.: Public opinion. In: The International Encyclopedia of Communication (2008)

    Google Scholar 

  27. Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  28. González, R.J.: Hacking the citizenry?: Personality profiling, ‘big data’ and the election of Donald Trump. Anthropol. Today 33(3), 9–12 (2017). https://doi.org/10.1111/1467-8322.12348

    Article  Google Scholar 

  29. Hamilton, D.L., Gifford, R.K.: Illusory correlation in interpersonal perception: a cognitive basis of stereotypic judgments. J. Exp. Soc. Psychol. 12(4), 392–407 (1976)

    Article  Google Scholar 

  30. He, C., Parra, D., Verbert, K.: Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)

    Article  Google Scholar 

  31. Hijikata, Y., Kai, Y., Nishida, S.: The relation between user intervention and user satisfaction for information recommendation. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC 2012, pp. 2002–2007. ACM, New York (2012). https://doi.org/10.1145/2245276.2232109

  32. Hmielowski, J.D., Hutchens, M.J., Cicchirillo, V.J.: Living in an age of online incivility: examining the conditional indirect effects of online discussion on political flaming. Inf. Commun. Soc. 17(10), 1196–1211 (2014)

    Article  Google Scholar 

  33. Hoff, R., Stroh, W., Zimmermann, M.: Divus augustus (2014)

    Google Scholar 

  34. Clemm von Hohenberg, B., Maes, M., Pradelski, B.S.: Micro influence and macro dynamics of opinion formation (2017). SSRN: https://ssrn.com/abstract=2974413 or https://doi.org/10.2139/ssrn.2974413

  35. Iyengar, S., Hahn, K.S.: Red media, blue media: evidence of ideological selectivity in media use. J. Commun. 59(1), 19–39 (2009)

    Article  Google Scholar 

  36. Jonas, E., Schulz-Hardt, S., Frey, D., Thelen, N.: Confirmation bias in sequential information search after preliminary decisions: an expansion of dissonance theoretical research on selective exposure to information. J. Pers. Soc. Psychol. 80(4), 557 (2001)

    Article  Google Scholar 

  37. Karlsson, N., Loewenstein, G., Seppi, D.: The ostrich effect: selective attention to information. J. Risk Uncertainty 38(2), 95–115 (2009)

    Article  MATH  Google Scholar 

  38. Knijnenburg, B.P., Reijmer, N.J., Willemsen, M.C.: Each to his own: how different users call for different interaction methods in recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 141–148. ACM, New York (2011). https://doi.org/10.1145/2043932.2043960

  39. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1–2), 101–123 (2012)

    Article  Google Scholar 

  40. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Nat. Acad. Sci. 110(15), 5802–5805 (2013)

    Article  Google Scholar 

  41. Lee, J.K., Choi, J., Kim, C., Kim, Y.: Social media, network heterogeneity, and opinion polarization. J. Commun. 64(4), 702–722 (2014)

    Article  Google Scholar 

  42. LoBue, V.: And along came a spider: an attentional bias for the detection of spiders in young children and adults. J. Exp. Child Psychol. 107(1), 59–66 (2010)

    Article  Google Scholar 

  43. Macy, M.W., Kitts, J.A., Flache, A., Benard, S.: Polarization in dynamic networks: a hopfield model of emergent structure. In: Dynamic Social Network Modeling and Analysis, pp. 162–173 (2003)

    Google Scholar 

  44. Marchi, R.: With Facebook, blogs, and fake news, teens reject journalistic “objectivity”. J. Commun. Inquiry 36(3), 246–262 (2012)

    Article  Google Scholar 

  45. Mark, N.: Beyond individual differences: social differentiation from first principles. Am. Soc. Rev. 63, 309–330 (1998)

    Article  Google Scholar 

  46. Mäs, M., Opp, K.D.: When is ignorance bliss? Disclosing true information and cascades of norm violation in networks. Soc. Netw. 47, 116–129 (2016)

    Article  Google Scholar 

  47. McCombs, M.E., Shaw, D.L.: The agenda-setting function of mass media. Public Opinion Q. 36(2), 176–187 (1972)

    Article  Google Scholar 

  48. McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems, pp. 1097–1101. ACM (2006)

    Google Scholar 

  49. Myers, J.H., Robertson, T.S.: Dimensions of opinion leadership. J. Mark. Res. 9, 41–46 (1972)

    Article  Google Scholar 

  50. Noelle-Neumann, E.: Die Schweigespirale. Piper (1980)

    Google Scholar 

  51. Nowak, A., Szamrej, J., Latané, B.: From private attitude to public opinion: a dynamic theory of social impact. Psychol. Rev. 97(3), 362 (1990)

    Article  Google Scholar 

  52. O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, IUI 1005, pp. 167–174. ACM, New York (2005). https://doi.org/10.1145/1040830.1040870

  53. O’Hara, K., Stevens, D.: Echo chambers and online radicalism: assessing the internet’s complicity in violent extremism. Policy Internet 7(4), 401–422 (2015)

    Article  Google Scholar 

  54. Pariser, E.: The Filter Bubble: What the Internet is Hiding from You. Penguin, London (2011)

    Google Scholar 

  55. Park, B., Rothbart, M.: Perception of out-group homogeneity and levels of social categorization: memory for the subordinate attributes of in-group and out-group members. J. Pers. Soc. Psychol. 42(6), 1051 (1982)

    Article  Google Scholar 

  56. Pei, C., et al.: Value-aware recommendation based on reinforcement profit maximization. In: The World Wide Web Conference, pp. 3123–3129. ACM (2019)

    Google Scholar 

  57. Pronin, E., Lin, D.Y., Ross, L.: The bias blind spot: perceptions of bias in self versus others. Pers. Soc. Psychol. Bull. 28(3), 369–381 (2002)

    Article  Google Scholar 

  58. Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 157–164. ACM (2011)

    Google Scholar 

  59. Quandt, T., Frischlich, L., Boberg, S., Schatto-Eckrodt, T.: Fake news. The International Encyclopedia of Journalism Studies, pp. 1–6 (2019)

    Google Scholar 

  60. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  61. Rogers, E.M., Cartano, D.G.: Methods of measuring opinion leadership. Public Opinion Q. 26, 435–441 (1962)

    Article  Google Scholar 

  62. Santos, R.L., Macdonald, C., Ounis, I.: Selectively diversifying web search results. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1179–1188. ACM (2010)

    Google Scholar 

  63. Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., Simons, A.: Ease of retrieval as information: another look at the availability heuristic. J. Pers. Soc. Psychol. 61(2), 195 (1991)

    Article  Google Scholar 

  64. Smaldino, P., Pickett, C., Sherman, J., Schank, J.: An agent-based model of social identity dynamics. J. Artif. Soc. Soc. Simul. 15(4), 7 (2012)

    Article  Google Scholar 

  65. Spohr, D.: Fake news and ideological polarization: filter bubbles and selective exposure on social media. Bus. Inf. Rev. 34(3), 150–160 (2017)

    Google Scholar 

  66. Suiter, J., Farrell, D.M., O’Malley, E.: When do deliberative citizens change their opinions? Evidence from the irish citizens’ assembly. Int. Polit. Sci. Rev. 37(2), 198–212 (2016)

    Article  Google Scholar 

  67. Taylor, P.D., Jonker, L.B.: Evolutionary stable strategies and game dynamics. Math. Biosci. 40(1–2), 145–156 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  68. Tinghuai, M., et al.: Social network and tag sources based augmenting collaborative recommender system. IEICE Trans. Inf. Syst. 98(4), 902–910 (2015)

    Google Scholar 

  69. Verbert, K., Parra, D., Brusilovsky, P., Duval, E.: Visualizing recommendations to support exploration, transparency and controllability. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp. 351–362. ACM (2013)

    Google Scholar 

  70. Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)

    Article  Google Scholar 

  71. Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)

    Article  Google Scholar 

  72. Zaller, J.: Political awareness, elite opinion leadership, and the mass survey response. Soc. Cogn. 8(1), 125–153 (1990)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to André Calero Valdez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Calero Valdez, A. (2020). Human and Algorithmic Contributions to Misinformation Online - Identifying the Culprit. In: Grimme, C., Preuss, M., Takes, F., Waldherr, A. (eds) Disinformation in Open Online Media. MISDOOM 2019. Lecture Notes in Computer Science(), vol 12021. Springer, Cham. https://doi.org/10.1007/978-3-030-39627-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-39627-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39626-8

  • Online ISBN: 978-3-030-39627-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics