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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. Technical report, National Bureau of Economic Research (2017)
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)
Berger, J., Milkman, K.L.: What makes online content viral? J. Mark. Res. 49(2), 192–205 (2012)
Bessi, A., Ferrara, E.: Social bots distort the 2016 us presidential election online discussion. First Monday 21(11-7) (2016)
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
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)
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
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)
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)
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
Childers, T.L.: Assessment of the psychometric properties of an opinion leadership scale. J. Mark. Res. 23, 184–188 (1986)
Deffuant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Adv. Complex Syst. 3(01n04), 87–98 (2000)
DeGroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc. 69(345), 118–121 (1974)
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)
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)
Dretske, F.: Knowledge and the Flow of Information. MIT Press, Cambridge (1981)
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
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)
Floridi, L.: Electronic Library. Brave. net. world: the internet as a disinformation superhighway? 14(6), 509–514 (1996)
Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333 (2014)
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)
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)
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)
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)
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)
Glynn, C.J., Huge, M.E.: Public opinion. In: The International Encyclopedia of Communication (2008)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
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
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)
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)
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
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)
Hoff, R., Stroh, W., Zimmermann, M.: Divus augustus (2014)
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
Iyengar, S., Hahn, K.S.: Red media, blue media: evidence of ideological selectivity in media use. J. Commun. 59(1), 19–39 (2009)
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)
Karlsson, N., Loewenstein, G., Seppi, D.: The ostrich effect: selective attention to information. J. Risk Uncertainty 38(2), 95–115 (2009)
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
Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1–2), 101–123 (2012)
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)
Lee, J.K., Choi, J., Kim, C., Kim, Y.: Social media, network heterogeneity, and opinion polarization. J. Commun. 64(4), 702–722 (2014)
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)
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)
Marchi, R.: With Facebook, blogs, and fake news, teens reject journalistic “objectivity”. J. Commun. Inquiry 36(3), 246–262 (2012)
Mark, N.: Beyond individual differences: social differentiation from first principles. Am. Soc. Rev. 63, 309–330 (1998)
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)
McCombs, M.E., Shaw, D.L.: The agenda-setting function of mass media. Public Opinion Q. 36(2), 176–187 (1972)
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)
Myers, J.H., Robertson, T.S.: Dimensions of opinion leadership. J. Mark. Res. 9, 41–46 (1972)
Noelle-Neumann, E.: Die Schweigespirale. Piper (1980)
Nowak, A., Szamrej, J., Latané, B.: From private attitude to public opinion: a dynamic theory of social impact. Psychol. Rev. 97(3), 362 (1990)
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
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)
Pariser, E.: The Filter Bubble: What the Internet is Hiding from You. Penguin, London (2011)
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)
Pei, C., et al.: Value-aware recommendation based on reinforcement profit maximization. In: The World Wide Web Conference, pp. 3123–3129. ACM (2019)
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)
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)
Quandt, T., Frischlich, L., Boberg, S., Schatto-Eckrodt, T.: Fake news. The International Encyclopedia of Journalism Studies, pp. 1–6 (2019)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Rogers, E.M., Cartano, D.G.: Methods of measuring opinion leadership. Public Opinion Q. 26, 435–441 (1962)
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)
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)
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)
Spohr, D.: Fake news and ideological polarization: filter bubbles and selective exposure on social media. Bus. Inf. Rev. 34(3), 150–160 (2017)
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)
Taylor, P.D., Jonker, L.B.: Evolutionary stable strategies and game dynamics. Math. Biosci. 40(1–2), 145–156 (1978)
Tinghuai, M., et al.: Social network and tag sources based augmenting collaborative recommender system. IEICE Trans. Inf. Syst. 98(4), 902–910 (2015)
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)
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)
Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)
Zaller, J.: Political awareness, elite opinion leadership, and the mass survey response. Soc. Cogn. 8(1), 125–153 (1990)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
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)