Ethics and Information Technology

, Volume 15, Issue 3, pp 209–227 | Cite as

Bias in algorithmic filtering and personalization

  • Engin BozdagEmail author
Original Paper


Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping.


Information politics Bias Social filtering Algorithmic gatekeeping 



The author would like to thank Martijn Warnier and Ibo van de Poel for their valuable comments. This research is supported by the Netherlands Organization for Scientific Research (NWO) Mozaiek grant, file number 017.007.111.


  1. Accuracast. (2010). Facebook advertising policies homophobic. May.
  2. Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23(1), 103–145.CrossRefGoogle Scholar
  3. Agada, J. (1999). Inner-city gatekeepers: An exploratory survey of their information use environment. Journal of the American Society for Information Science, 50(1), 74–85. Scholar
  4. Albanesius, C. (2011). Schmidt, yelp clash over google’s search tactics. PCMAG.,2817,2393369,00.asp.
  5. Althaus, S. L., & Tewksbury, D. (2002). Agenda setting and the ‘new’ news. Communication Research, 29(2), 180.CrossRefGoogle Scholar
  6. Badash, D. (2011). Has facebook censorship gone too far? The New Civil Rights Movement.
  7. Bagdikian, B. H. (2004). The New media monopoly: A completely revised and updated edition with seven new chapters. Beacon Press, May.
  8. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In Proceedings of the 21st international conference on World Wide Web (WWW '12) (pp. 519–528). New York, NY, USA: ACM. doi: 10.1145/2187836.2187907.
  9. Bar-Ilan, J., Keenoy, K., Levene, M., & Yaari, E. (2009). Presentation bias is significant in determining user preference for search results—A user study. Journal of the American Society for Information Science and Technology, 60(1), 135–149.CrossRefGoogle Scholar
  10. Barzilai-Nahon, K. (2008). Toward a theory of network gatekeeping: A framework for exploring information control. Journal of the American Society for Information Science and Technology, 59(9), 1493–1512.CrossRefGoogle Scholar
  11. Barzilai-Nahon, K. (2009). Gatekeeping: A critical review. Annual Review of Information Science and Technology, 43(1), 1–79.CrossRefGoogle Scholar
  12. Benkler, Y. (2006). The wealth of networks: How social production transforms markets and freedom. New Haven: Yale University Press.Google Scholar
  13. Bruns, A. (2008). Gatewatching, gatecrashing: Futures for tactical news media. In M. Boler (Ed.), Digital media and democracy: Tactics in hard times (pp. 247–271). MIT Press.
  14. Bruns, A. (2011). Gatekeeping, gatewatching, real-time feedback. Brazilian Journalism Research, 7, 117–136Google Scholar
  15. Chatman, E. A. (1987). Opinion leadership, poverty, and information sharing. RQ, 26(3), 53–341.
  16. Chen, C. C., & Hernon, P. (1982). Information seeking: Assessing and anticipating user needs. Neal-Schuman Publishers.
  17. Chibnall S. (1975}. The crime reporter: A study in the production of commercial knowledge. Sociology, 9(1), 49–66.Google Scholar
  18. Chilling effects. (2005). Scientology complains that advanced technology appears in Google groups.
  19. Christman, J. (2011). Autonomy in moral and political philosophy. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Stanford, CA: CSLI, Stanford University.Google Scholar
  20. Costolo, D. (2011). The trends are algorithmic, not chosen by us but we edit out any w/obscenities. July.!/dickc/status/97686216681594880.
  21. Cuilenburg, V. (1999). On competition, access and diversity in media, old and new some remarks for communications policy in the information age. New Media & Society, 1(2), 183–207.CrossRefGoogle Scholar
  22. Dekker, V. (2006). Google: Een zwijgzame rechter en politieagent. Trouw.Google Scholar
  23. Diaz, A. (2008). Through the Google goggles: Sociopolitical bias in search engine design. In S. Amanda., & Z. Michael (Eds.), Information science and knowledge management (Vol. 14, pp. 11–34). Berlin Heidelberg: Springer.Google Scholar
  24. Durrance, J. C. (1984). Armed for action library response to citizen information needs. New York, NY: Neal Schuman.Google Scholar
  25. Edelman, B. (2011). Bias in search results: Diagnosis and response. Indian Journal of Law and Technology, 7, 16.Google Scholar
  26. Efrati, A. (2010). Rivals say Google plays favourites. Wall Street Journal, December.
  27. Elgan, M. (2011). How to pop your Internet ‘filter bubble’. Computerworld.
  28. Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, mis, and related disciplines. The Information Society, 20(5), 325–344.CrossRefGoogle Scholar
  29. Fishman, M. (1988). Manufacturing the news. Austin: University of Texas Press.Google Scholar
  30. Flanagan, M., Howe, D., & Nissenbaum, H. (2008) Embodying values in technology: Theory and practice. In J. van den Hoven & J. Weckert (Eds.), Information technology and moral philosophy (pp. 322–353). Cambridge: Cambridge University Press.Google Scholar
  31. Fong, J. (2011). Facebook’s bias against 3rd party apps.
  32. Foundem. 2009. Foundem’s Google story.Google Scholar
  33. Friedman, B., Kahn, P. H., & Alan, B. (2006). Value sensitive design and information systems. Human-Computer Interaction in Management Information Systems: Foundations, 4, 348–372.Google Scholar
  34. Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330–347.CrossRefGoogle Scholar
  35. Friedman, B., & Nissenbaum, H. (1997). Software agents and user autonomy. In Proceedings of the first international conference on autonomous agentsAGENTS’97, pp. 466–469.Google Scholar
  36. Gans, H. J. (2005). Deciding what’s news: A study of CBS evening news, NBC nightly news, newsweek, and time (2nd ed.). Evanston: Northwestern University Press.Google Scholar
  37. Garcia-Molina, H., Koutrika, G., & Parameswaran, A. (2011). Information seeking. Communications of the ACM, 54(11), 121. doi: 10.1145/2018396.2018423.CrossRefGoogle Scholar
  38. Gauch, S., Speretta, M., Chandramouli, A., & Micarelli, A. (2007). User profiles for personalized information access. The adaptive web (pp. 54–89). Berlin Heidelberg: Springer.Google Scholar
  39. Gillespie, T. (2012). Can an algorithm be wrong? Limn (2).
  40. Goldman, E. (2005). Search engine bias and the demise of search engine utopianism. Yale JL & Technology, 8, 188.Google Scholar
  41. Goldman, E. (2011). Revisiting search engine bias chapter in (Contemporary Issues in Cyberlaw), William Mitchell Law Review, 38, 96–110.Google Scholar
  42. Google. (2008). We knew the web was big.
  43. Google. (2012). Search plus your world: Personal results.
  44. Granka, L. A. (2010). The politics of search: A decade retrospective. The Information Society, 26(5), 364–374. doi: 10.1080/01972243.2010.511560.CrossRefGoogle Scholar
  45. Granovetter, M. S. (1981). The strength of weak ties: a network theory revisited. State University of New York, Department of Sociology.Google Scholar
  46. Groot, J. (2004). Trouw wekenlang niet te vinden op Google. Webwereld.Google Scholar
  47. Guha, S., Cheng, B., & Francis, P. (2010). Challenges in measuring online advertising systems. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement (IMC '10) (pp. 81–87). New York, NY, USA: ACM. doi: 10.1145/1879141.1879152.
  48. Helberger, N. (2011). Diversity by design. Journal of Information Policy, 1, 441–469.Google Scholar
  49. Hermida, A. (2012). Tweets and truth: Journalism as a discipline of collaborative verification. Journalism Practice, 6(5-6), 659–668.CrossRefGoogle Scholar
  50. Hilbert, M. (2012). Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making. Psychological Bulletin, 138(2), 211–237.MathSciNetCrossRefGoogle Scholar
  51. Hitwise. (2010). Social networks now more popular than search engines in the UK.Google Scholar
  52. Hoven, J. V., & Rooksby, E. (2008). Distributive justice and the value of information: A (broadly) Rawlsian approach. England: Cambridge University Press.Google Scholar
  53. IBM. (2011). Bringing smarter computing to big data.Google Scholar
  54. Ingram, M. (2011). The downside of facebook as a public space: Censorship. June.
  55. Jacobs, G. (2010). Techradar: How to optimise your site for Google Caffeine., April.
  56. Joachims, T., & Radlinski, F. (2007). Search engines that learn from implicit feedback. Computer, 40(8), 34–40.CrossRefGoogle Scholar
  57. Katz, E. (1996). And deliver us from segmentation. Annals of the American Academy of Political and Social Science, 546, 22–33.CrossRefGoogle Scholar
  58. Katz, E., & Lazarsfeld, P. (2005). Personal influence: The part played by people in the flow of mass communications. New Jersey: Transaction Publishers.Google Scholar
  59. Kincaid, J. (2010). Techcrunch/today’s lesson: Make facebook angry, and they’ll censor you into oblivion. TechCrunch.
  60. Klein, J. (2011). A web marketer’s guide to reddit. December.
  61. Knight, W. (2012). Google hopes to make friends with a more social search: technology review. Technology Review.
  62. Korolova, A. (2010). Privacy violations using microtargeted ads: A case study. In Proceedings of the IEEE international conference on data mining workshops (ICDMW '10) (pp. 474–482). Washington, DC, USA: IEEE Computer Society. doi: 10.1109/ICDMW.2010.137.
  63. Lasorsa, D. L., Lewis, S. C., & Holton, A. (2012). Normalizing Twitter-Journalism practice in an emerging communication space. Journalism Studies, 13(1), 19–36.CrossRefGoogle Scholar
  64. Lavie, T., Sela, M., Oppenheim, I., Inbar, O., & Meyer, J. (2009). User attitudes towards news content personalization. International Journal of Human-Computer Studies, 68(8), 483–495.CrossRefGoogle Scholar
  65. Levinson, P. (1999). Digital McLuhan: A guide to the information millennium (1st ed.). London: Routledge.Google Scholar
  66. Lotan, G. (2011). Data reveals that “occupying” twitter trending topics is harder than it looks!
  67. Lu, Y. (2007). The human in human information acquisition: Understanding gatekeeping and proposing new directions in scholarship. Library & Information Science Research, 29(1), 103–123.CrossRefGoogle Scholar
  68. Manyika, J., Chui, M., Brown, B., Buighin, J., Dobbs, R., & Roxburgh, C. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute report. Whitereport. Downloadable at
  69. Metz, C. (2011a). Google opens curtain on ‘manual’ search penalties. The register.
  70. Metz, C. (2011b). Google contradicts own counsel in face of antitrust probe, admits existence of search algorithm whitelists.
  71. Morozov, E. (2011). Your own facts. Book review of ‘the filter bubble, what the internet is hiding from you’. The New York Times.Google Scholar
  72. Mowshowitz, A., & Kawaguchi, A. (2002). Bias on the web. Communications of the ACM, 45(9), 56–60.CrossRefGoogle Scholar
  73. Munson, S. Z., & Resnick, P. (2010). Presenting diverse political opinions: How and how much CHI’10. In Proceedings of the SIGCHI conference on human factors in computing systems. Google Scholar
  74. Nagesh, G. (2011). Privacy advocates want facebook probed on recent changes.Google Scholar
  75. Napoli, P. (1999). Deconstructing the diversity principle. Journal of Communication, 49(4), 7–34.CrossRefGoogle Scholar
  76. Nissenbaum, H., & Introna, L. D. (2000). Shaping the web: Why the politics of search engines matters. The Information Society, 16(3), 169–185.CrossRefGoogle Scholar
  77. O’Dell, J. (2011). Facebook’s ad revenue hit $1.86B for 2010. Mashable.Google Scholar
  78. Opsahl, K. (2009). Google begins behavioural targeting ad program.
  79. Pariser, E. (2011a). 10 ways to pop your filter bubble.
  80. Pariser, E. (2011b). The filter bubble: What the internet is hiding from you. London: Penguin Press.Google Scholar
  81. Priestley, M. (1999). Honest news in the slashdot decade. First Monday, 4, 2–8.CrossRefGoogle Scholar
  82. Resnick, P., Lacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on computer supported cooperative work (CSCW '94) (pp. 175–186). New York, NY, USA: ACM. doi: 10.1145/192844.192905.
  83. Salihefendic, A. (2010). How reddit ranking algorithms work.
  84. Schroeder, S. (2011). Twitter ad revenue may reach $150 million this year. Mashable.Google Scholar
  85. Searchenginewatch. (2012). Twitter: Google search plus your world bad for web users. Search Engine Watch.
  86. Segal, D. (2011). Search optimization and its dirty little secrets. The New York Times.Google Scholar
  87. Shardanand, U., & Maes, P. (1995). Social information filtering: algorithms for automating “word of mouth”. In I. R. Katz, R. Mack, L. Marks, M. B Rosson & J. Nielsen (Eds.), Proceedings of the SIGCHI conference on human factors in computing systems (CHI '95) (pp. 210–217). New York, NY, USA: ACM Press/Addison-Wesley Publishing Co. doi: 10.1145/223904.223931.
  88. Shoemaker, P. J, Vos, T., & Reese, P. (2008). Journalists as gatekeepers. In K. W. Jorgensen., & T. Hanitzsch (Eds.). The handbook of journalism studies (pp. 73–87). New York: RoutledgeGoogle Scholar
  89. Shoemaker, P. J., & Vos, T. (2009). Gatekeeping theory (1st ed.). London: Routledge.Google Scholar
  90. Slater, P. E. (1955). Role differentiation in small groups. American Sociological Review, 20(3), 300–310.MathSciNetCrossRefGoogle Scholar
  91. Smith, J., McCarthy, J. D., McPhail, C., & Augustyn, B. (2001). From protest to agenda building: Description bias in media coverage of protest events in Washington, D.C. Social Forces, 79(4), 1397–1423.CrossRefGoogle Scholar
  92. Smyth, B. (2007). A community-based approach to personalizing web search. Computer, 40(8), 42–50. doi: 10.1109/MC.2007.259.CrossRefGoogle Scholar
  93. Soley, L. C. (2002). Censorship Inc: The corporate threat to free speech in the United States. USA: Monthly Review Press.Google Scholar
  94. Sturges, P. (2001). Gatekeepers and other intermediaries. Aslib Proceedings, 53(2), 62–67.CrossRefGoogle Scholar
  95. Sullivan, D. (2012). Google’s results get more personal with ‘search plus your world’. Search engine land.
  96. Sunstein, C. R. (2002). USA: Princeton University Press.Google Scholar
  97. Sunstein, C. (2006). Preferences, paternalism, and liberty. Royal Institute of Philosophy Supplements, 59, 233–264.CrossRefGoogle Scholar
  98. Sunstein, C. R. (2008). Infotopia: How many minds produce knowledge. USA: Oxford University Press.Google Scholar
  99. Taylor, D. (2011). Everything you need to know about facebook’s edgerank. The Next Web.
  100. Techcrunch. (2011). Edgerank: The secret sauce that makes facebook’s news feed tick.Google Scholar
  101. Tewksbury, D. (2003). What do Americans really want to know? Tracking the behavior of news readers on the internet. Journal of Communication, 53(4), 694–710.CrossRefGoogle Scholar
  102. Twitter. (2010). To trend or not to trend.
  103. Tynan, D. (2012). How companies buy facebook friends, likes, and buzz. PCWorld.Google Scholar
  104. Upbin, B. (2011). Facebook ushers in era of new social gestures—Forbes. Forbes.Google Scholar
  105. US Securities and Exchange Commission. (2009). Google Inc., Consolidated Balance Sheets.
  106. Van Couvering, E. (2007). Is relevance relevant? Market, science, and war: discourses of search engine quality. Journal of Computer-Mediated Communication, 12(3), 866.CrossRefGoogle Scholar
  107. Van der Hof, S., & Prins, C. (2008). Personalisation and its influence on identities, behaviour and social values. Profiling the European citizen: Cross-disciplinary perspectives (pp. 111–127). Netherlands: Springer.Google Scholar
  108. Vaughan, L., & Thelwall, M. (2004). Search engine coverage bias: Evidence and possible causes. Information Processing and Management, 40(4), 693–707.CrossRefGoogle Scholar
  109. Witten, I. A. (2007). Bias, privacy, and personalization on the web. In M. Sankara Reddy & H. Kumar (Eds.), E-libraries: Problems and perspectives. New Delhi: Allied.Google Scholar
  110. Wittman, C. (2011). Comments 4x more valuable than likes.
  111. Wright, J. D. (2011) Defining and measuring search bias: Some preliminary evidence. International center for law & economics, November 2011; George Mason Law & Economics Research Paper No. 12–14. Available at SSRN:
  112. Yu, C., Lakshmanan, L., & Amer-Yahia, S. (2009). It takes variety to make a world: Diversification in recommender systems. In Proceedings of the 12th international conference on extending database technology: Advances in database technology (pp. 368–378).
  113. Yue, Y., Patel, R., & Roehrig, H. (2010). Beyond position bias: Examining result attractiveness as a source of presentation bias in click through data. In Proceedings of the 19th international conference on World wide web (pp. 1011–1018).
  114. Zhang, M., & Hurley, N. (2008). Avoiding monotony: Improving the diversity of recommendation lists, 2008 ACM international conference on recommender systems (ACM Recsys’08) (pp. 123–130). Switzerland: Lausanne.CrossRefGoogle Scholar
  115. Zimmer, M. (2011). Facebook’s censorship problem. June.

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands

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