Overcoming bias to learn about controversial topics

Journal of the Association for Information Science and Technology

Volumn 66, Issue 8, 2015, Pages 1655-1672

  Vydiswaran, V G Vinod ^a   Zhai, Chengxiang ^b   Roth, Dan ^b   Pirolli, Peter ^c  

^a UNIVERSITY OF MICHIGAN   (United States)

^b University of Illinois at Urbana Champaign   (United States)

^c PALO ALTO RESEARCH CENTER   (United States)

Author keywords

content analysis; human computer interaction; information dissemination

Indexed keywords

DATA MINING; INFORMATION DISSEMINATION;

BUILD SYSTEMS; CONFIRMATION BIAS; CONTENT ANALYSIS; CONTROVERSIAL TOPICS; EVIDENCE DOCUMENTS; EXPLICIT KNOWLEDGE; HUMAN BIAS; USER STUDY;

HUMAN COMPUTER INTERACTION;

EID: 84946213712     PISSN: 23301635     EISSN: 23301643     Source Type: Journal    
DOI: 10.1002/asi.23274     Document Type: Article

References (39)

1. Baron, J., (2000). Thinking and deciding. New York: Cambridge Press.
2. Black, D., (1987). The theory of committees and elections. Boston, MA: Kluwer Academic Publishers.
3. Blei, D.M., Ng, A.Y., Jordan, M.I., &, Lafferty, J., (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
4. Croft, W.B., (2000). Combining approaches to information retrieval. In, W.B. Croft, (Ed.), Advances in information retrieval: Recent research from the Centre for Intelligent Information Retrieval (p. 1-36). Boston, MA: Kluwer.
5. Danielson, D.R., (2005). Web credibility. In, C. Ghaoui, (Ed.), Encyclopedia of human-computer interaction (pp. 713-721). Hershey, PA: Idea Group.
6. Fogg, B.J., (2003). Persuasive technology: Using computers to change what we think we do. San Francisco, CA: Morgan Kaufmann.
7. Fogg, B.J., &, Tseng, H., (1999). The elements of computer credibility. In, M.G. Williams, &, M.W. Altom, (Eds.), Proceedings of the SIGCHI conference on Human Factors in Computing Systems (CHI) (pp. 80-87). New York: ACM.
8. Fogg, B.J., Marshall, J., Laraki, O., Osipovich, A., Varma, C., Fang, N.,..., Treinen, M., (2001). What makes Web sites credible? A report on a large quantitative study. In, J. Jacko, &, A. Sears, (Eds.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI) (pp. 61-68). New York: ACM.
9. Gallard, A., Abiteboul, S., Marian, A., &, Senellart, P., (2010). Corroborating information from disagreeing views. In, N. Craswell, &, B. Liu, (Eds.), Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM) (pp. 131-140). New York: ACM.
10. Hofmann, T., (1999). Probabilistic latent semantic indexing. In, F. Gey, M. Hearst, &, R. Tong, (Eds.), Proceedings of 22nd ACM International Conference on Research and development in Information Retrieval (SIGIR) (pp. 50-57). New York: ACM.
11. Kaiser Family Foundation. (2004). Seniors and the Internet. Survey conducted by Kaiser Family Foundation and Princeton Survey Research Associates International, March 5-April 18, 2004. USPSRA2004-ENT031. Retrieved from the iPOLL Databank, The Roper Center for Public Opinion Research, University of Connecticut.
12. Kaiser Family Foundation. (2009). 2009 Survey of Americans on HIV/AIDS-Toplines. Survey conducted by Henry J. Kaiser Family Foundation, January 26-March 8, 2009. Retrieved from the iPOLL Databank, The Roper Center for Public Opinion Research, University of Connecticut.
13. Leiserowitz, A., Maibach, E., Roser-Renouf, C., &, Hmielowski, J.D., (2011). Politics and global warming: Democrats, Republicans, Independents, and the Tea Party. Yale University and George Mason University. New Haven, CT: Yale Project on Climate Change Communication. Retrieved from http://environment.yale.edu/climate/files/PoliticsGlobalWarming2011.pdf
14. Lemur Project. (2002). The Lemur Toolkit. Retrieved from http://www.lemurproject.org/
15. Lewandowsky, S., Ecker, U.K.H., Seifert, C.M., Schwarz, N., &, Cook, J., (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13 (3), 106-131.
16. Metzger, M.J., Flanagin, A.J., Eyal, K., Lemus, D.R., &, McCann, R.M., (2003). Credibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment. In, P.J. Kalbfleisch, (Ed.), Communications yearbook, 27 (pp. 293-335). Mahwah, NJ: Lawrence Erlbaum.
17. Nickerson, R.S., (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2 (2), 175-220.
18. Pariser, E., (2011). The filter bubble: What the Internet is hiding from you. New York: Penguin Press.
19. Pew Research Center for the People and the Press. (2010). Americans spending more time following the news-Ideological news sources: Who watches and why. Pew Research Center Biennial News Consumption Survey.
20. Pirolli, P., Wollny, E., &, Suh, B., (2009). So you know you're getting the best possible information: A tool that increases Wikipedia credibility. In, D. Gilmore, (Ed.), Proceedings of the 27th Annual SIGCHI Conference on Human Factors in Computing Systems (pp. 1505-1508). New York: ACM.
21. Plous, S., (1993). The psychology of judgment and decision making. New York: McGraw-Hill.
22. Prior, M., (2003). Liberated viewers, polarized voters: The implications of increased media choice for democratic politics. The Good Society, 11 (3), 10-16.
23. ProCon.org (2011). ProCon.org. Retrieved from http://www.procon.org/
24. PSRA/Newsweek. (2000). PSRA/Newsweek Poll # 2000-05: K-8 Parents. Survey of national adult parents of children in grades K-8, conducted by Newsweek and Princeton Survey Research Associates, February 5-February 10, 2000 [USPSRA2000-NW05]. Retrieved from the iPOLL Databank, The Roper Center for Public Opinion Research, University of Connecticut.
25. PSRA/Newsweek/Kaplan Education Center. (1998). Survey of national adult parents of children in grades K-8, conducted by Newsweek, Kaplan Educational Centers, March 5-March 10, 1998. [USPSRNEW.031298.R16]. Retrieved from the iPOLL Databank, The Roper Center for Public Opinion Research, University of Connecticut.
26. Rieh, S.Y., (2002). Judgment of information quality and cognitive authority in the Web. Journal of the American Society for Information Science and Technology, 53 (2), 145-161.
27. Rieh, S.Y., &, Belkin, N.J., (1998). Understanding judgment of information quality and cognitive authority in the WWW. In, C.M. Preston, (Ed.), Proceedings of the 61st Annual Meeting of the American Society for Information Science (ASIS) (pp. 279-289). Medford, NJ: Information Today.
28. Rieh, S.Y., &, Danielson, D.R., (2007). Credibility: A multidisciplinary framework. In, B. Cronin, (Ed.), Annual Review of Information Science and Technology, 41 (pp. 307-364). Medford, NJ: Information Today.
29. Savolainen, R., (2011). Judging the quality and credibility of information in Internet discussion forums. Journal of the American Society for Information Science and Technology, 62 (7), 1243-1256.
30. Suh, B., Chi, E.H., Kittur, A., &, Pendleton, B.A., (2008). Lifting the veil: Improving accountability and social transparency in Wikipedia with WikiDashboard. In Proceeding of the 26th annual SIGCHI Conference on Human factors in Computing Systems (pp. 1037-1040). New York: ACM.
31. Taber, C.S., &, Lodge, M., (2006). Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50 (3), 755-769.
32. Taylor, H., (2011). The growing influence and use of health care information obtained online. The Harris Poll® #98, published on September 15, 2011. Harris interactive.
33. Ugander, J., Backstrom, L., Marlow, C., &, Kleinberg, J., (2012). Structural diversity in social contagion. In, I.M. Verma, (Ed.), Proceedings of the National Academy of Sciences of the United States of America, 109 (16), 5961-5966.
34. Vydiswaran, V.G.V., Zhai, C., &, Roth, D., (2011). Gauging the Internet doctor: Ranking medical claims based on community knowledge. In, N. Chawla, et al. (Eds.), Proceedings of the KDD Workshop on Data Mining for Medicine and HealthCare (pp. 42-51). New York: ACM.
35. Vydiswaran, V.G.V., Zhai, C., Roth, D., &, Pirolli, P., (2012a). BiasTrust: Teaching biased users about controversial topics. In, G. Lebanon, H. Wang, &, M.J. Zaki, (Eds.), Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM) (pp. 1205-1209). New York: ACM.
36. Vydiswaran, V.G.V., Zhai, C., Roth, D., &, Pirolli, P., (2012b). Unbiased learning of controversial topics. In, A. Grove, (Ed.), Proceedings of the 75th Annual Meeting of the American Society for Information Science and Technology (ASIS&T) (pp. 291.1-291.4). Silver Spring, MD: Richard B. Hill, Association for Information Science and Technology.
37. Wathen, C.N., &, Burkell, J., (2002). Believe it or not: Factors influencing credibility on the Web. Journal of the American Society for Information Science and Technology, 53 (2), 134-144.
38. Wu, M., &, Marian, A., (2007). Corroborating answers from multiple Web sources. In, M. Benedikt, &, I. Manolescu, (Eds.), Proceedings of the 10th International Workshop on Web and Databases (WebDB) (pp. 1-6). New York: ACM.
39. Yin, X., Han, J., &, Yu, P.S., (2008). Truth discovery with multiple conflicting information providers on the Web. IEEE Transactions on Knowledge and Data Engineering, 20 (6), 796-808.