Browse > Article

Fake News in Social Media: Bad Algorithms or Biased Users?  

Zimmer, Franziska (Department of Information Science, Heinrich Heine University)
Scheibe, Katrin (Department of Information Science, Heinrich Heine University)
Stock, Mechtild (Stock-Kerpen)
Stock, Wolfgang G. (Department of Information Science, Heinrich Heine University)
Publication Information
Journal of Information Science Theory and Practice / v.7, no.2, 2019 , pp. 40-53 More about this Journal
Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic construction of filter bubbles, and (2) Are echo chambers of fake news manmade, and if yes, what are the information behavior patterns of those individuals reacting to fake news? We discuss the role of filter bubbles by analyzing social media's ranking and results' presentation algorithms. To understand the roles of individuals in the process of making and cultivating echo chambers, we empirically study the effects of fake news on the information behavior of the audience, while working with a case study, applying quantitative and qualitative content analysis of online comments and replies (on a blog and on Reddit). Indeed, we found hints on filter bubbles; however, they are fed by the users' information behavior and only amplify users' behavioral patterns. Reading fake news and eventually drafting a comment or a reply may be the result of users' selective exposure to information leading to a confirmation bias; i.e. users prefer news (including fake news) fitting their pre-existing opinions. However, it is not possible to explain all information behavior patterns following fake news with the theory of selective exposure, but with a variety of further individual cognitive structures, such as non-argumentative or off-topic behavior, denial, moral outrage, meta-comments, insults, satire, and creation of a new rumor.
fake news; truth; information behavior; social media; filter bubble; echo chamber;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Hays, P. A. (2004). Case study research. In K. deMarrais & S. D. Lapan (Eds.), Foundations for research (pp. 217-234). Mahwah: Lawrence Erlbaum.
2 Hern, A. (2017, May 22). How social media filter bubbles and algorithms influence the election. The Guardian, Retrieved Apr 18, 2019 from
3 Holmes, R. (2016, December 8). The problem isn't fake news, it's bad algorithms: Here's why. Observer. Retrieved Apr 18, 2019 from
4 Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288.   DOI
5 Hyman, H. H., & Sheatsley, P. B. (1947). Some reasons why information campaigns fail. Public Opinion Quarterly, 11(3), 412-423.   DOI
6 Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., ... Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences of the United States of America, 113(3), 554-559.   DOI
7 DiFranzo, D., & Gloria-Garcia, K. (2017). Filter bubbles and fake news. XRDS: Crossroads, ACM Magazine for Students, 23(3), 32-35.
8 Dubois, E., & Blank, G. (2018). The echo chamber is overstated: The moderating effect of political interest and diverse media. Information, Communication & Society, 21(5), 729-745.   DOI
9 Elo, S., & Kyngas, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107-115.   DOI
10 Fischer, P., Lea, S., Kastenmuller, A., Greitemeyer, T., Fischer, J., & Frey, D. (2011). The process of selective exposure: Why confirmatory information search weakens over time. Organizational Behavior and Human Decision Making, 114(1), 37-48.   DOI
11 Frey, D. (1986). Recent research on selective exposure to information. Advances in Experimental Social Psychology, 19, 41-80.   DOI
12 Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298-320.   DOI
13 Floridi, L. (2005). Is information meaningful data? Philosophy and Phenomenological Research, 70(2), 351-370.   DOI
14 Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219-245.   DOI
15 Bruns, A. (2019). Are filter bubbles real? Cambridge: Polity Press.
16 Garrett, R. K. (2009). Echo chambers online? Politically motivated selective exposure among Internet new users. Journal of Computer-Mediated Communication, 14(2), 265-285.   DOI
17 Brentano, F. (1930). Wahrheit und Evidenz. Leipzig: Meiner.
18 Bruns, A. (2017). Echo chamber? What echo chamber? Reviewing the evidence. In 6th Biennal Future of Journalism Conference (FOJ17), 14-15 September, Cardiff, UK. Retrieved Apr 18, 2019 from
19 Buckland, M. K. (1991). Information and information systems. New York: Praeger.
20 Case, D. O., & Given, L.M. (2018). Looking for information: A survey of research on information seeking, needs, and behavior (4th ed.). Bingley: Emerald.
21 Chisholm, R. M. (1977). Theory of knowledge. Englewood Cliffs: Prentice-Hall.
22 Connaway, L. S., Julien, H., Seadle, M., & Kasprak, A. (2017). Digital literacy in the era of fake news: Key roles for information professionals. Proceedings of the Association for Information Science and Technology, 54(1), 554-555.
23 Torres, R. R., Gerhart, N., & Negahban, A. (2018). Combating fake news: An investigation of information verification behaviors on social networking sites. In Proceedings of the 51st Hawaii International Conference on System Sciences (pp. 3976-3985). Honolulu: HICSS.
24 Conroy, N. J., Rubin, V. L., & Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1), 1-4.
25 David, M. (1994). Correspondence and disquotation. An essay on the nature of truth. Oxford: Oxford University Press.
26 Zimmer, F., Scheibe, K., Stock, M., & Stock, W. G. (2019). Echo chambers and filter bubbles of fake news in social media. Man-made or produced by algorithms? In 8th Annual Arts, Humanities, Social Sciences & Education Conference (pp. 1-22). Honolulu: Hawaii University.
27 Zimmer, F., Scheibe, K., & Stock, W. G. (2018). A model for information behavior research on social live streaming services (SLSSs). In Meiselwitz G. (Ed.), Social Computing and Social Media. Technologies and Analytics: 10th International Conference, Part II (pp. 429-448). Cham: Springer.
28 Zuckerberg, M., Sanghvi, R., Bosworth, A., Cox, C., Sittig, A., Hughes, C., ... Corson, D. (2006). Dynamically providing a news feed about a user of a social network, U.S. Patent No. 7,669,123 B2. Washington, DC: U.S. Patent and Trademark Office.
29 Tseng, E. (2015). Providing relevant notifications based on common interests in a social networking system, U.S. Patent No. 9,083,767. Washington, DC: U.S. Patent and Trademark Office.
30 Volkova, S., & Jang, J. Y. (2018). Misleading or falsification: Inferring deceptive strategies and types in online news and social media. In Companion Proceedings of the Web Conference 2018 (pp. 575-583). Geneva: International World Wide Web Conferences Steering Committee.
31 Vydiswaran, V. G. V., Zhai, C. X., Roth, D., & Pirolli, P. (2012). BiasTrust: Teaching biased users about controversial content. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 1905-1909). New York: ACM.
32 Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211-236.   DOI
33 Davies, H. C. (2018). Redefining filter bubbles as (escapable) socio-technical recursion. Sociological Research Online, 23(3), 637-654.   DOI
34 Berelson, B. (1952). Content analysis in communications research. Glencoe: Free Press.
35 Bessi, A., Zollo, F., Del Vicario, M., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Trend of narratives in the age of misinformation. PLoS ONE, 10(8), e0134641.   DOI
36 Bakshy, E., Messing, S., & Adamic, L. A. (2015). Political science: Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132.   DOI
37 Bastos, M., Mercea, D., & Baronchelli, A. (2018). The geographic embedding of online echo chambers: Evidence from the Brexit campaign. PLoS One, 13(11), e0206841.   DOI
38 Batchelor, O. (2017). Getting out the truth: The role of libraries in the fight against fake news. Reference Services Review, 45(2), 143-148.   DOI
39 Walter, S., Bruggemann, M., & Engesser, S. (2018). Echo chambers of denial: Explaining user comments on climate change. Environmental Communication, 12(2), 204-217.   DOI
40 Vydiswaran, V. G. V., Zhai, C. X., Roth, D., & Pirolli, P. (2015). Overcoming bias to learn about controversial topics. Journal of the Association for Information Science and Technology, 66(8), 1655-1672.   DOI
41 Zimmer, F. (2019). Fake news. Unbelehrbar in der Echokammer? In W. Bredemeier (Ed.), Zukunft der Informationswissenschaft. Hat die Informationswissenschaft eine Zukunft? (pp. 393-399). Berlin: Simon Verlag fur Bibliothekswissen.
42 Sears, D. O., & Freedman, J. L. (1967). Selective exposure to information: A critical review. Public Opinion Quarterly, 31(2), 194-213.   DOI
43 Zimmer, F., Akyurek, H., Gelfart, D., Mariami, H., Scheibe, K., Stodden, R., ... Stock, W. G. (2018). An evaluation of the social news aggregator Reddit. In 5th European Conference on Social Media (pp. 364-373). Sonning Common: Academic Conferences and Publishing International.
44 Zimmer, F., & Reich, A. (2018). What is truth? Fake news and their uncovering by the audience. In 5th European Conference on Social Media (pp. 374-381). Sonning Common: Academic Conferences and Publishing International.
45 Seargeant, P., & Tagg, C. (2019). Social media and the future of open debate: A user-oriented approach to Facebook's filter bubble conundrum. Discourse, Context and Media, 27, 41-48.   DOI
46 Self, W. (2016, November 28). Forget fake news on Facebook: The real filter bubble is you. New Statesman America. Retrieved Apr 18, 2019 from
47 Shin, J., Jian, L., Driscoll, K., & Bar, F. (2018). The diffusion of misinformation on social media: Temporal pattern, message, and source. Computers in Human Behavior, 83, 278-287.   DOI
48 Soergel, D. (1994). Indexing and the retrieval performance: The logical evidence. Journal of the American Society for Information Science, 45(8), 589-599.   DOI
49 Spohr, D. (2017). Fake news and ideological polarization: Filter bubbles and selective exposure on social media. Business Information Review, 34(3), 150-160.   DOI
50 Stock, M. (2016). Facebook: A source for microhistory? In K. Knautz & K.S. Baran (Eds.), Facets of Facebook: Use and users (pp. 210-240). Berlin: De Gruyter Saur.
51 Stock, M. (2017). HCI research and history: Special interests groups on Facebook as historical sources. In HCI International 2017: Posters' Extended Abstracts. HCI 2017 (pp. 497-503). Cham: Springer.
52 Stock, W. G., & Stock, M. (2013). Handbook of information science. Berlin: De Gruyter Saur.
53 Tandoc Jr., E. C., Lim, Z. W., & Ling, R. (2018). Defining 'fake news.' A typology of scholarly definitions. Digital Journalism, 6(2), 137-153.   DOI
54 Tornberg, P. (2018). Echo chambers and viral misinformation: Modeling fake news as complex contagion. PLoS ONE, 13(9), e0203958.   DOI
55 Mayring, P., & Fenzl, T. (2019). Qualitative inhaltsanalyse. In N. Baur & J. Blasius (Eds.), Handbuch Methoden der empirischen Sozialforschung (pp. 633-648). Wiesbaden: Springer Fachmedien.
56 Mihailidis, P., & Viotty, S. (2017). Spreadable spectacle in digital culture: Civic expression, fake news, and the role of media literacy in 'post-fact' society. American Behavioral Scientist, 61(4), 441-454.   DOI
57 Munoz-Torres, J. R. (2012). Truth and objectivity in journalism. Anatomy of an endless misunderstanding. Journalism Studies, 13(4), 566-582.   DOI
58 Neurath, O. (1931). Soziologie im Physikalismus. Erkenntnis, 2(1), 393-431.   DOI
59 Murungi, D. M., Yates, D. J., Purao, S., Yu, Y. J., & Zhan, R. (2019). Factual or believable? Negotiating the boundaries of confirmation bias in online news stories. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 5186-5195). Honolulu: HICSS.
60 Nelson, J. L., & Webster, J. G. (2017). The myth of partisan selective exposure: A portrait of the online news audience. Social Media + Society, 3(3). doi:10.1177/2056305117729314.   DOI
61 Oxford Dictionaries (2016). Word of the year 2016 is post-truth. Retrieved Apr 18, 2019 from
62 Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. London: Viking.
63 Pawlow, T. (1973). Die Widerspiegelungstheorie. Berlin: Deutscher Verlag der Wissenschaften.
64 Quattrociocchi, W. (2017). Inside the echo chamber. Scientific American, 316(4), 60-63.   DOI
65 Russell, B. (1971). Problems in Philosophy. Oxford: Oxford University Press.
66 Saracevic, T. (1975). Relevance: A review of and a framework for the thinking on the notion in information science. Journal of the American Society for Information Science, 26(6), 321-343.   DOI
67 Kaplan, A. M., & Haenlein, M. (2010). Users of the world unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68.   DOI
68 Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). Los Angeles: Sage.
69 Kearney, M. W. (2019). Analyzing change in network polarization. New Media & Society, 21(6), 1380-1402.   DOI
70 Kim, A., & Dennis, A. R. (2018). Says who? How news presentation format influences perceived believability and the engagement level of social media users. In Proceedings of the 51st Hawaii International Conference on System Sciences (pp. 3955-3965). Washington, DC: IEEE Computer Science.
71 Kuhlen, R. (1995). Informationsmarkt. Chancen und Risiken der Kommerzialisierung von Wissen. Konstanz Germany: UVK.
72 Mans, S. (2016, November 13). In the filter bubble: How algorithms customize our access to information [Web blog post]. Retrieved Apr 18, 2019 from
73 Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017). Beyond misinformation: Understanding and coping with the 'post-truth' era. Journal of Applied Research in Memory and Cognition, 6(4), 353-369.   DOI
74 Liao, Q.V., & Fu, W.T. (2013). Beyond the filter bubble: Interactive effects of perceived threat and topic involvement on selective exposure to information. In Proceedings of the SIGHCI Conference on Human Factors in Computing Systems (pp. 2359-2368). New York: ACM.
75 Linde, F., & Stock, W. G. (2011). Information markets: A strategic guide for the i-commerce. Berlin: De Gruyter Saur.
76 Gilbert, E., Bergstrom, T., & Karahalios, K. (2009). Blogs are echo chambers: Blogs are echo chambers. In Proceedings of the 42nd Hawaii International Conference on System Sciences (pp. 1-10). Washington, DC: IEEE Computer Science.
77 Haim, M., Graefe, A., & Brosius, H. B. (2018). Burst of the filter bubble? Effects of personalization on the diversity of Google News. Digital Journalism, 6(3), 330-343.   DOI
78 Gust von Loh, S., & Stock, W. G. (2013). Informationskompetenz als Schulfach? In S. Gust von Loh & W. G. Stock (Eds.), Informationskompetenz in der Schule. Ein informationswissenschaftlicher Ansatz (pp. 1-20). Berlin: De Gruyter Saur.
79 Habermas, J. (1972). Knowledge and human interests. Boston: Beacon Press.
80 Habermas, J. (2006). Political communication in media society. Does democracy still enjoy an epistemic dimension? The impact of normative theory on empirical research. Communication Theory, 16(4), 411-426.   DOI