research-article

Making Sense of Conflicting Science Information: Exploring Bias in the Search Engine Result Page

Authors:

Eric MeyersAuthors Info & Claims

CHIIR '17: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

Pages 175 - 184

https://doi.org/10.1145/3020165.3020185

Published: 07 March 2017 Publication History

Abstract

Currently, there is widespread media coverage about the problems with 'fake news' that appears in social media, but the effects of biased information that appears in search engine results is also increasing. The authors argue that the search engine results page (SERP) exposes three important types of bias: source bias, algorithmic bias, and cognitive bias. To explore the relationship between these three types of bias, we conducted a mixed methods study with sixty participants (plus fourteen in a pilot to make a total of seventy-four participants). Within a library setting, participants were provided with mock search engine pages that presented order-controlled sources on a science controversy. Participants were then asked to rank the sources' usefulness and then summarize the controversy. We found that participants ranked the usefulness of sources depending on its presentation within a SERP. In turn, this also influenced how the participants summarized the topic. We attribute the differences in the participants' writings to the cognitive biases that affect a user's judgment when selecting sources on a SERP. We identify four main cognitive biases that a SERP can evoke in students: Priming, Anchoring, Framing, and the Availability Heuristic. While policing information quality is a quixotic task, changes can be made to both SERPs and a user's decision-making when selecting sources. As bias emerges both on the system side and the user side of search, we suggest a two-fold solution is required to address these challenges.

References

[1]

N. J. Belkin, M. Cole, and J. Liu, "A model for evaluation of interactive information retrieval," presented at the Proceedings of the SIGIR 2009 Workshop on the Future of IR Evaluation, 2009, pp. 7--8.

[2]

R. Epstein and R. E. Robertson, "The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections," Proc. Natl. Acad. Sci., p. 201419828, Aug. 2015.

[3]

E. Hargittai, "Digital Na(t)ives" Variation in Internet Skills and Uses among Members of the "Net Generation,"? Sociol. Inq., vol. 80, no. 1, pp. 92--113, Feb. 2010.

[4]

E. M. Meyers, "Access denied: how students resolve information needs when an ideal document is restricted," in Proceedings of the 2012 iConference, 2012, pp. 629--631.

Digital Library

[5]

"National Science Board (2014) Science and Engineering Indicators." National Science Foundation, Arlington, VA., 2014.

[6]

Q. Guo, R. W. White, S. T. Dumais, J. Wang, and B. Anderson, "Predicting query performance using query, result, and user interaction features," presented at the Adaptivity, Personalization and Fusion of Heterogeneous Information, 2010, pp. 198--201.

Digital Library

[7]

N. Höchstötter and D. Lewandowski, "What users see? Structures in search engine results pages," Inf. Sci., vol. 179, no. 12, pp. 1796--1812, 2009.

Digital Library

[8]

D. Kelly, "Methods for evaluating interactive information retrieval systems with users," Found. Trends Inf. Retr., vol. 3, no. 1--2, pp. 1--224, 2009.

Digital Library

[9]

A. Shiri and L. Zvyagintseva, "Dynamic Query Suggestion in Web Search Engines: A Comparative Examination," presented at the Proceedings of the Annual Conference of CAIS/Actes du congrès annuel de l-ACSI, 2014.

[10]

E. R. Tufte, Beautiful evidence. Cheshire, Conn: Graphics Press, 2006.

Digital Library

[11]

R. Cellan-Jones, "Six searches that show the power of Google," Apr. 2016.

[12]

S. Gerhart, "Do Web search engines suppress controversy",? First Monday, vol. 9, no. 1, 2004.

[13]

D. C. Mutz, Population-based survey experiments. Princeton University Press, 2011.

[14]

M. Schudson, "The 'Lippmann-Dewey Debate' and the Invention of Walter Lippmann as an Anti-Democrat 1985--1996," Int. J. Commun., vol. 2, p. 12, 2008.

[15]

J. W. Carey, Communication as Culture: Essays on Media and Society. Psychology Press, 1989.

[16]

J. Dewey and M. L. Rogers, The public and its problems: An essay in political inquiry. Penn State Press, 2012.

[17]

W. Lippmann, Public opinion. Transaction Publishers, 1946.

[18]

E. Amend and D. Barney, "Getting It Right: Canadian Conservatives and the 'War on Science'.," Can. J. Commun., vol. 41, no. 1, 2016.

[19]

S. Dunwoody, "Science journalism," Handb. Public Commun. Sci. Technol., pp. 15--26, 2008.

[20]

E. Segev and A. J. Sharon, "Temporal patterns of scientific information-seeking on Google and Wikipedia," Public Underst. Sci., p. 963662516648565, 2016.

[21]

B. Latour, Science in action: How to follow scientists and engineers through society. Harvard university press, 1987.

[22]

L. Daston and P. Galison, "The Image of Objectivity," Representations, no. 40, pp. 81--128, Oct. 1992.

[23]

T. Bubela et al., "Science communication reconsidered," Nat. Biotechnol., vol. 27, no. 6, pp. 514--518, 2009.

[24]

L. D. Carsten and D. L. Illman, "Perceptions of accuracy in science writing," Prof. Commun. IEEE Trans. On, vol. 45, no. 3, pp. 153--156, 2002.

[25]

S. Dunwoody and M. Ryan, "Scientific barriers to the popularization of science in the mass media," J. Commun., vol. 35, no. 1, pp. 26--42, 1985.

[26]

McComas and Simone, "Media Coverage of Conflict Science Communication," Vol 24 No, vol. 24, no. 4, Jun. 2003.

[27]

D. Nelkin, Selling Science - How the Press Covers Science and Technology. New York: W.H. Freeman and Company, 1987.

[28]

D. A. Scheufele, "Science communication as political communication," Proc. Natl. Acad. Sci., vol. 111, no. Supplement_4, pp. 13585--13592, Sep. 2014.

[29]

G. C. Bowker and S. L. Star, Sorting things out classification and its consequences. Cambridge, Mass.: MIT Press, 1999.

Digital Library

[30]

E. Morozov, The net delusion: the dark side of internet freedom, 1. ed. New York, NY: Public Affairs, 2011.

Digital Library

[31]

B. J. Fogg, Persuasive Technology: Using Computers to Change what We Think and Do. Morgan Kaufmann, 2003.

Digital Library

[32]

B. Friedman and H. Nissenbaum, "Bias in computer systems," ACM Trans. Inf. Syst. TOIS, vol. 14, no. 3, pp. 330--347, 1996.

Digital Library

[33]

D. Kahneman and A. Tversky, "Subjective probability: A judgment of representativeness," Cognit. Psychol., vol. 3, no. 3, pp. 430--454, 1972.

[34]

D. Kahneman and A. Tversky, "On the reality of cognitive illusions.," 1996.

[35]

D. Kahneman, Thinking, fast and slow. 2013.

[36]

S. Lewandowsky, U. K. H. Ecker, C. M. Seifert, N. Schwarz, and J. Cook, "Misinformation and Its Correction: Continued Influence and Successful Debiasing," Psychol. Sci. Public Interest, vol. 13, no. 3, pp. 106--131, Dec. 2012.

[37]

P. C. Wason and D. Shapiro, "Natural and contrived experience in a reasoning problem," Q. J. Exp. Psychol., vol. 23, no. 1, pp. 63--71, Feb. 1971.

[38]

C. Behnert and D. Lewandowski, "Ranking Search Results in Library Information Systems -- Considering Ranking Approaches Adapted From Web Search Engines," J. Acad. Librariansh., vol. 41, no. 6, pp. 725--735, Nov. 2015.

[39]

L. Lorigo et al., "Eye tracking and online search: Lessons learned and challenges ahead," J. Am. Soc. Inf. Sci. Technol., vol. 59, no. 7, pp. 1041--1052, 2008.

Digital Library

[40]

B. Pan, H. Hembrooke, T. Joachims, L. Lorigo, G. Gay, and L. Granka, "In google we trust: Users' decisions on rank, position, and relevance," J. Comput. Commun., vol. 12, no. 3, pp. 801--823, 2007.

[41]

T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay, "Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search," ACM Trans. Inf. Syst. TOIS, vol. 25, no. 2, p. 7, 2007.

Digital Library

[42]

R. Fidel, Human information interaction: an ecological approach to information behavior. MIT Press, 2012.

[43]

D. O. Case, Looking for Information: A Survey of Research on Information Seeking, Needs and Behavior. Emerald Group Publishing, 2012.

[44]

G. B. Chapman and E. J. Johnson, "Incorporating the irrelevant: Anchors in judgments of belief and value," Heuristics Biases Psychol. Intuitive Judgm., pp. 120--138, 2002.

[45]

J. S. B. T. Evans and D. E. Over, Rationality and Reasoning. Psychology Press, 2013.

[46]

R. Cellan-Jones, "The Force of Google," BBC Radio 4, May-2016.

[47]

R. B. Zajonc, "Mere exposure: A gateway to the subliminal," Curr. Dir. Psychol. Sci., vol. 10, no. 6, pp. 224--228, 2001.

[48]

J. A. Bargh, P. M. Gollwitzer, A. Lee-Chai, K. Barndollar, and R. Trötschel, "The automated will: nonconscious activation and pursuit of behavioral goals.," J. Pers. Soc. Psychol., vol. 81, no. 6, p. 1014, 2001.

[49]

C. R. Mynatt, M. E. Doherty, and R. D. Tweney, "Confirmation bias in a simulated research environment: An experimental study of scientific inference," Q. J. Exp. Psychol., vol. 29, no. 1, pp. 85--95, Feb. 1977.

[50]

C. A. Chinn, "Factors that Influence," presented at the Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society: June 18 to 21, 1993, Institute of Cognitive Science, University of Colorado-Boulder, 1993, vol. 15, p. 318.

[51]

L. Salmerón, Y. Kammerer, and P. García-Carrión, "Searching the Web for conflicting topics: Page and user factors," Comput. Hum. Behav., vol. 29, no. 6, pp. 2161--2171, Nov. 2013.

Digital Library

[52]

E. Agichtein, E. Brill, and S. Dumais, "Improving web search ranking by incorporating user behavior information," presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 19--26.

Digital Library

[53]

J. Liu and N. J. Belkin, "Personalizing information retrieval for multi-session tasks: Examining the roles of task stage, task type, and topic knowledge on the interpretation of dwell time as an indicator of document usefulness," J. Assoc. Inf. Sci. Technol., vol. 66, no. 1, pp. 58--81, 2015.

Digital Library

[54]

N. J. Belkin, M. Cole, and J. Liu, "A model for evaluation of interactive information retrieval," presented at the Proceedings of the SIGIR 2009 Workshop on the Future of IR Evaluation, 2009, pp. 7--8.

[55]

D. Case, "Sixty years of measuring the use of information and its sources: from consultation to application," Libr. Digit. Age LIDA Proc., vol. 13, 2014.

[56]

D. O. Case and L. G. O'Connor, "What's the use? Measuring the frequency of studies of information outcomes: Journal of the Association for Information Science and Technology," J. Assoc. Inf. Sci. Technol., vol. 67, no. 3, pp. 649--661, Mar. 2016.

Digital Library

[57]

B. Pan, H. Hembrooke, T. Joachims, L. Lorigo, G. Gay, and L. Granka, "In Google We Trust: Users' Decisions on Rank, Position, and Relevance," J. Comput.-Mediat. Commun., vol. 12, no. 3, pp. 801--823, Apr. 2007.

[58]

Google, The Evolution of Search. 2011.

[59]

A. Tversky and D. Kahneman, "Judgment under uncertainty: Heuristics and biases," science, vol. 185, no. 4157, pp. 1124--1131, 1974.

[60]

M. Hilbert, "Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making.," Psychol. Bull., vol. 138, no. 2, pp. 211--237, 2012.

[61]

E. Yong, "Psychologists strike a blow for reproducibility," Nature, Nov. 2013.

[62]

C. Ware, Information visualization: perception for design, Third edition. Waltham, MA: Morgan Kaufmann, 2013.

Digital Library

[63]

D. Kahneman, A. Treisman, and B. J. Gibbs, "The reviewing of object files: Object-specific integration of information," Cognit. Psychol., vol. 24, no. 2, pp. 175--219, Apr. 1992.

[64]

N. Höchstötter and D. Lewandowski, "What users see -- Structures in search engine results pages," Inf. Sci., vol. 179, no. 12, pp. 1796--1812, May 2009.

Digital Library

[65]

M. R. Laurent and T. J. Vickers, "Seeking health information online: does Wikipedia matter",? J. Am. Med. Inform. Assoc., vol. 16, no. 4, pp. 471--479, 2009.

[66]

J. Braun and T. Gillespie, "Hosting the public discourse, hosting the public: When online news and social media converge," Journal. Pract., vol. 5, no. 4, pp. 383--398, 2011.

[67]

A. Diaz, "Through the Google Goggles: Sociopolitical Bias in Search Engine Design," in Web search: multidisciplinary perspectives, A. Spink and M. Zimmer, Eds. Berlin: Springer, 2008, pp. 11--34.

[68]

T. Gillespie, "The politics of 'platforms,'" New Media Soc., vol. 12, no. 3, pp. 347--364, May 2010.

[69]

T. Harper, "The big data public and its problems: Big data and the structural transformation of the public sphere," New Media Soc., p. 1461444816642167, 2016.

[70]

P. Ladwig, A. A. Anderson, D. Brossard, D. A. Scheufele, and B. Shaw, "Narrowing the nano discourse?," Mater. Today, vol. 13, no. 5, pp. 52--54, 2010.

[71]

B. Rieder and G. Sire, "Conflicts of interest and incentives to bias: A microeconomic critique of Google's tangled position on the Web," New Media Soc., p. 1461444813481195, 2013.

[72]

C. Lauckner and G. Hsieh, "The presentation of health-related search results and its impact on negative emotional outcomes," in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2013, pp. 333--342.

Digital Library

[73]

D. Kahneman, "Maps of bounded rationality: A perspective on intuitive judgment and choice," Nobel Prize Lect., vol. 8, pp. 351--401, 2002.

[74]

D. A. Scheufele and D. Tewksbury, "Framing, Agenda Setting, and Priming: The Evolution of Three Media Effects Models," J. Commun., vol. 57, no. 1, pp. 9--20, Mar. 2007.

[75]

J. Hullman and N. Diakopoulos, "Visualization rhetoric: Framing effects in narrative visualization," IEEE Trans. Vis. Comput. Graph., vol. 17, no. 12, pp. 2231--2240, 2011.

Digital Library

[76]

R. Bauman, "Genre," J. Linguist. Anthropol., vol. 9, no. 1/2, pp. 84--87, 1999.

[77]

Y.-C. Huang, H.-H. Chen, M.-L. Yeh, and Y.-C. Chung, "Case studies combined with or without concept maps improve critical thinking in hospital-based nurses: A randomized-controlled trial," Int. J. Nurs. Stud., vol. 49, no. 6, pp. 747--754, 2012.

[78]

P. Borlund, "Interactive Information Retrieval: An Introduction," J. Inf. Sci. Theory Pract., vol. 1, no. 3, pp. 12--32, Sep. 2013.

[79]

K. B. Rasmussen, "General Approaches to Data Quality and Internet-generated Data," in The Sage Handbook of online Research Methods, Fielding, Lee, and Blank, Eds. Sage, 2008.

[80]

J. Creswell, Research Design - Qualitative, Quantitative, and Mixed Methods Approach, vol. 3. Sage, 2014.

[81]

M. A. Carriquiry, X. Du, and G. R. Timilsina, "Second generation biofuels: Economics and policies," Spec. Sect. Renew. Energy Policy Dev., vol. 39, no. 7, pp. 4222--4234, Jul. 2011.

[82]

A. B. Delshad, L. Raymond, V. Sawicki, and D. T. Wegener, "Public attitudes toward political and technological options for biofuels," Energy Policy, vol. 38, no. 7, pp. 3414--3425, Jul. 2010.

[83]

H. Longstaff, D. M. Secko, G. Capurro, P. Hanney, and T. McIntyre, "Fostering citizen deliberations on the social acceptability of renewable fuels policy: The case of advanced lignocellulosic biofuels in Canada," Biomass Bioenergy, vol. 74, pp. 103--112, Mar. 2015.

[84]

T. E. McKone et al., "Grand Challenges for Life-Cycle Assessment of Biofuels," Env. Sci Technol, vol. 45, no. 5, pp. 1751--1756, 2011.

[85]

D. M. Secko and E. Einsiedel, "The biofuels quadrilemma, public perceptions and policy," Biofuels, vol. 5, no. 3, pp. 207--209, May 2014.

[86]

D. Smith, "BFN Annual Report 2015," 2015. {Online}. Available: http://www.biofuelnet.ca/wp-content/uploads/2013/02/BFN-2015-Annual-Report_11.10.15_web.pdf. {Accessed: 25-Oct-2015}.

[87]

National Resources Canada, "Biodiesel - Government Programs and Regulations | Natural Resources Canada," 2015. {Online}. Available: http://www.nrcan.gc.ca/energy/alternative-fuels/fuel-facts/biodiesel/3515. {Accessed: 06-Mar-2016}.

[88]

G. Hotchkiss, T. Sherman, R. Tobin, C. Bates, and K. Brown, "Search engine results: 2010," Enq. Search Solut., pp. 1--61, 2010.

[89]

K. Popper, Realism and the aim of science: From the postscript to the logic of scientific discovery. Routledge, 2013.

[90]

J. Wood, D. Badawood, J. Dykes, and A. Slingsby, "BallotMaps: Detecting name bias in alphabetically ordered ballot papers," IEEE Trans. Vis. Comput. Graph., vol. 17, no. 12, pp. 2384--2391, 2011.

Digital Library

[91]

E.-J. Wagenmakers, R. Wetzels, D. Borsboom, H. L. J. van der Maas, and R. A. Kievit, "An Agenda for Purely Confirmatory Research," Perspect. Psychol. Sci., vol. 7, no. 6, pp. 632--638, Nov. 2012.

[92]

M. L. Berger, M. Mamdani, D. Atkins, and M. L. Johnson, "Good Research Practices for Comparative Effectiveness Research: Defining, Reporting and Interpreting Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report-Part I," Value Health, vol. 12, no. 8, pp. 1044--1052, Nov. 2009.

[93]

A. Collins, J. Greeno, L. Resnick, B. Berliner, and R. Calfee, "Cognition and learning," B Berl. R Calfee Handb. Educ. Psychol. N. Y. Simon Shuster MacMillan, 1992.

[94]

E. M. Meyers, "Losses and gains in collaborative search: Insights from the Middle School Classroom," GROUP'10 Novemb. 7--12 2010 Sanibel Isl. FL USA, 2010.

[95]

D. M. Levy and C. C. Marshall, "Going digital: a look at assumptions underlying digital libraries," Commun. ACM, vol. 38, no. 4, pp. 77--84, Apr. 1995.

Digital Library

[96]

P. Dillenbourg and M. Baker, "Negotiation spaces in human-computer collaborative learning," in Proceedings of the International Conference on Cooperative Systems, 1996, pp. 12--14.

[97]

J. Foster, Ed., Collaborative information behavior: user engagement and communication sharing. Hershey PA: Information Science Reference, 2010.

[98]

P. Hansen, C. Shah, and C.-P. Klas, Eds., Collaborative Information Seeking. Cham: Springer International Publishing, 2015.

Digital Library

[99]

M. R. Morris, "Collaborative search revisited," in Proceedings of the 2013 conference on Computer supported cooperative work, 2013, pp. 1181--1192.

Digital Library

[100]

M. B. Twidale, D. M. Nichols, and C. D. Paice, "Browsing is a collaborative process," Inf. Process. Manag., vol. 33, no. 6, pp. 761--783, 1997.

Digital Library

[101]

J. D. Novak and A. J. Cañas, "The theory underlying concept maps and how to construct and use them," Fla. Inst. Hum. Mach. Cogn., vol. 2008, 2008.

[102]

J. D. Novak, Learning how to learn. Cambridge {Cambridgeshire}?; New York: Cambridge University Press, 1984.

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Vidal-Sepúlveda MOlivares-Rodríguez C(2024)Mediación algorítmica: sesgos en la búsqueda de información en estudiantes universitariosEuropean Public & Social Innovation Review10.31637/epsir-2024-3979(1-18)Online publication date: 19-Jul-2024
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Index Terms

Making Sense of Conflicting Science Information: Exploring Bias in the Search Engine Result Page
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query representation

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CHIIR '17: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

March 2017

454 pages

ISBN:9781450346771

DOI:10.1145/3020165

Conference Chairs:
Ragnar Nordlie
Oslo & Akershus University College of Applied Sciences, Norway
,
Nils Pharo
Oslo and Akershus University College of Applied Sciences, Norway
,
Program Chairs:
Luanne Freund
University of British Columbia, Canada
,
Birger Larsen
Aalborg University, Denmark
,
Dan Russel
Google, USA

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https://doi.org/10.1016/B978-0-323-95689-5.00238-8
Vidal-Sepúlveda MOlivares-Rodríguez C(2024)Mediación algorítmica: sesgos en la búsqueda de información en estudiantes universitariosEuropean Public & Social Innovation Review10.31637/epsir-2024-3979(1-18)Online publication date: 19-Jul-2024
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https://dl.acm.org/doi/10.1145/3626772.3661382
Kawai DSoska KRoutledge BZetlin-Jones AChristin N(2024)Stranger Danger? Investor Behavior and Incentives on Cryptocurrency Copy-Trading PlatformsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642715(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642715
Knight SBowdler IFord HZhou J(2024)A visual scoping review of how knowledge graphs and search engine results page designs represent uncertainty and disagreementInformation and Learning Sciences10.1108/ILS-02-2024-0016125:11/12(1030-1053)Online publication date: 14-Aug-2024
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