research-article

Can you hear me now?: mitigating the echo chamber effect by source position indicators

Authors:

Wai-Tat FuAuthors Info & Claims

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

Pages 184 - 196

https://doi.org/10.1145/2531602.2531711

Published: 15 February 2014 Publication History

Abstract

We examined how a source position indicator showing both valences (pro/con) and magnitudes (moderate/extreme) of positions on controversial topics influenced users' selection and reception of diverse opinions in online discussions. Results showed that the indicator had differential impact on participants who had varied levels of accuracy motives -- i.e., motivation to accurately learn about the topic, by leading to greater exposure to attitude-challenging information for participants with higher accuracy motives. Further analysis revealed that it was mainly caused by the fact that the presence of position indicator increased the selection of moderately inconsistent sources for participants with high accuracy motives but decreased the selection of them for participants with low accuracy motives. The indicator also helped participants differentiate between sources with moderate and extreme positions, and increased their tendency to agree with attitude-challenging information from sources with moderately inconsistent positions. Participants with high accuracy motives were also found to learn significantly more about the arguments put forward by the opposite side with the help of the position indicator. We discussed the implications of the results for the nature of the echo chamber effect, as well as for designing information systems that encourage seeking of diverse information and common ground seeking.

References

[1]

Van Alstyne, M., & Brynjolfsson, E. (1996). Electronic communities: Global village or cyberbalkans?. In Proceedings of the 17th International Conference on Information Systems.

[2]

Aronson, E., Turner, J. A., & Carlsmith, J. M. (1963). Communicator credibility and communication discrepancy as determinants of opinion change. The Journal of Abnormal and Social Psychology, 67(1), 31.

[3]

Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), 15

[4]

=Cronbach, L.J., Coefficient Alpha and The Internal Structure of Tests. Psychometrika 16, 3 (1951), 297334.

[5]

Faridani, S., Bitton, E., Ryokai, K., & Goldberg, K. (2010). Opinion space: a scalable tool for browsing online comments. In Proc. CHI2010, 1175--1184).

Digital Library

[6]

Fischer, P. (2011). Selective Exposure, Decision Uncertainty, and Cognitive Economy: A New Theoretical Perspective on Confirmatory Information Search. Social and Personality Psychology Compass, 5(10), 751--762.

[7]

Fischhoff, B., Slovic, P., & Lichtenstein, S. (1977). Knowing with certainty: The appropriateness of extreme confidence. Journal of Experimental Psychology: Human Perception and Performance, 3(4), 552.

[8]

Frey, D. Recent Research on Selective Exposure to Information. Advances in Experimental Social Psychology 19(1986), 41--80.

[9]

Garrett, R., Resnick, P. Resisting Political Fragmentation on the Internet. Daedalus 140, 4 (2011) 108--120.

[10]

Garrett, R. K., Stroud,N. (2012). Decoupling selective approach and selective avoidance. Paper to be presented at the National Communication Association, Political Communication Division, Orlando, FL.

[11]

Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: a meta-analysis of selective exposure to information. Psychological bulletin, 135(4), 555.

[12]

Himmelfarb, S. The measurement of attitudes. In A.H. Eagly & S. Chaiken (Eds.), Psychology of Attitudes (1993), 23--88. Thomson/Wadsworth

[13]

Janis, I. L., & Mann, L. (1977). Decision making: A psychological analysis of conflict, choice, and commitment (p. 81). New York: Free Press.

[14]

Jiang, Y., Liao, Q., Cheng, Q., Berlin, R., Schatz, B. Designing and Evaluating a Clustering System for Organizing and Integrating Patient Drug Outcomes in Personal Health Messages. In Proc. AMIA 2012

[15]

Klayman, J. (1995). Varieties of confirmation bias. Psychology of learning and motivation, 32, 385418.

[16]

Kriplean, T., Morgan, J., Freelon, D., Borning, A., Bennett, L. Supporting Reflective Public Thought with ConsiderIt. In Proc. CSCW2012, 265--274

Digital Library

[17]

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 Proc. CHI2013, 2359--2368.

Digital Library

[18]

Maass, A., & Clark, R. D. (1984). Hidden impact of minorities: Fifteen years of minority influence research. Psychological Bulletin, 95(3), 428--450.

[19]

Mendelberg, T. (2002). The deliberative citizen: Theory and evidence. Political decision making, deliberation and participation, 6(1), 151--193.

[20]

Mulvenna, M. D., Anand, S. S., & Büchner, A. G. (2000). Personalization on the Net using Web mining: introduction. Communications of the ACM, 43(8), 122125.

Digital Library

[21]

Munson, S. A., Lee, S. Y., & Resnick, P. (2013). Encouraging Reading of Diverse Political Viewpoints with a Browser Widget. In Proc. ICWSM 2013

[22]

Munson, S. & Resnick P. Presenting Diverse Political Opinions: How and How Much. In Proc. CHI2010, 1457--1466.

Digital Library

[23]

Nemeth, C. J., & Kwan, J. L. (1987). Minority influence, divergent thinking and detection of correct solutions. Journal of Applied Social Psychology, 17(9), 788--799.

[24]

Oh, A., Lee, H., Kim, Y. User Evaluation of a System for Classifying and Displaying Political Viewpoints of Weblogs. In Proc. ICWSM 2009, 282--285

[25]

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis.Foundations and trends in information retrieval, 2(1--2), 1--135.

Digital Library

[26]

Pariser, E. The Filter Bubble: What the Internet Is Hiding from You, Penguin Press, 2011

Digital Library

[27]

Park, S., Kang, S., Chung, S., Song, J. NewsCube: Delivering Multiple Aspects of News to Mitigate Media Bias. In Proc. CHI2009, 443--452

Digital Library

[28]

Petty, R. E., & Krosnick, J. A. (Eds.). (1995). Attitude strength: Antecedents and consequences (Vol. 4). Mahwah, NJ: Lawrence Erlbaum Associates

[29]

Sanders, GS; Mullen, B. 1982. Accuracy in perceptions of consensus: Differential tendencies of people with majority and minority positions. Eur J Soc Psychol 13(1): 57--70.

[30]

Sears, D. O., & Freedman, J. L. (1967). Selective exposure to information: A critical review. Public Opinion Quarterly, 31(2), 194--213.

[31]

Sunstein, C. R. (2009). Going to Extremes: How Like Minds Unite and Divide: How Like Minds Unite and Divide. Oxford University Press, USA.

[32]

Sweeny, K., Melnyk, D., Miller, W., & Shepperd, J. A. (2010). Information avoidance: Who, what, when, and why. Review of general psychology, 14(4), 340--353

[33]

Valentino, N. A., Banks, A. J., Hutchings, V. L., & Davis, A. K. (2009). Selective exposure in the Internet age: The interaction between anxiety and information utility. Political Psychology, 30(4), 591--613.

[34]

Vydiswaran, V., Zhai, C., Roth, D., Pirolli, P. Unbiased Learning of Controversial Topics. In Proc. ASIST2012

[35]

Yatani, K., Novati M., Trusty, A., Truong, K. Review Spotlight: A User Interface for Summarizing User Generated Reviews Using Adjective-Noun Word Pairs. In Proc. CHI'11, 1541--1550

Digital Library

[36]

Zhou, D. X., Resnick, P., & Mei, Q. (2011, May). Classifying the political leaning of news articles and users from user votes. In Proc. ICWSM2011, 417--424. APPENDIX A: TOPICS USED IN THE EXPERIMENT 1. Should death penalty be allowed? 2. Should prescription drugs be advertised directly to consumers' 3. Should euthanasia be legal? 4. Do violent video games contribute to the increase of youth violence? 5. Should people become vegetarian? 6. Should social security be privatized?

Cited By

Ghafouri VAlatawi FKarami MSuch JSuarez-Tangil G(2024)Transformer-Based Quantification of the Echo Chamber Effect in Online CommunitiesProceedings of the ACM on Human-Computer Interaction10.1145/36870068:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687006
Frappier TBressa NHuron S(2024)Jumping to Conclusions: A Visual Comparative Analysis of Online Debate Platform LayoutsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685377(1-15)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685377
Mao DWu WDu D(2024)Co-Activity Maximization in Online Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321326011:1(66-75)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3213260
Show More Cited By

Index Terms

Can you hear me now?: mitigating the echo chamber effect by source position indicators
1. Applied computing
  1. Document management and text processing
    1. Document preparation
      1. Multi / mixed media creation
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Hypertext / hypermedia

Recommendations

"Why would anybody do this?": Understanding Older Adults' Motivations and Challenges in Crowd Work
CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Diversifying participation in crowd work can benefit the worker and requester. Increasing numbers of older adults are online, but little is known about their awareness of or how they engage in mainstream crowd work. Through an online survey with 505 ...
Investigating the Motivational Paths of Peer Production Newcomers
CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Maintaining participation beyond the initial period of engagement is critical for peer production systems. Theory suggests that an increase in motivation is expected with contributors' movement from the community periphery to the core. Less is known, ...
Tailoring coaching strategies to users’ motivation in a multi-agent health coaching application
Abstract
Embodied conversational agents are often included in health behaviour change applications as intelligent virtual coaches. A major challenge in their development is tailoring coaching dialogues to user profiles. Agents should collect ...
Highlights
- Tailoring of coaching strategies is explored in a multi-agent application.
- ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

February 2014

1600 pages

ISBN:9781450325400

DOI:10.1145/2531602

General Chairs:
Susan Fussell
Cornell University
,
Wayne Lutters
University of Maryland, Baltimore County
,
Program Chairs:
Meredith Ringel Morris
Microsoft Research
,
Madhu Reddy
Penn State University

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CSCW'14

Sponsor:

SIGCHI

CSCW'14: Computer Supported Cooperative Work

February 15 - 19, 2014

Maryland, Baltimore, USA

Acceptance Rates

CSCW '14 Paper Acceptance Rate 134 of 497 submissions, 27%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

Upcoming Conference

CSCW '25

Sponsor:
sigchi

Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

43
Total Citations
View Citations
1,240
Total Downloads

Downloads (Last 12 months)162
Downloads (Last 6 weeks)11

Reflects downloads up to 03 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ghafouri VAlatawi FKarami MSuch JSuarez-Tangil G(2024)Transformer-Based Quantification of the Echo Chamber Effect in Online CommunitiesProceedings of the ACM on Human-Computer Interaction10.1145/36870068:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687006
Frappier TBressa NHuron S(2024)Jumping to Conclusions: A Visual Comparative Analysis of Online Debate Platform LayoutsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685377(1-15)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685377
Mao DWu WDu D(2024)Co-Activity Maximization in Online Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321326011:1(66-75)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3213260
Noda KFukuma TUbukata TIchikawa YKambe KMasubuchi YToriumi F(2024)Polarization Prism: Facilitating Diverse Viewpoint Reflection by Mining Unseen Perspectives in Social Media Polarization2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825907(3026-3033)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825907
Erickson JYan BHuang J(2023)Bridging Echo Chambers? Understanding Political Partisanship through Semantic Network AnalysisSocial Media + Society10.1177/205630512311863689:3Online publication date: 2-Aug-2023
https://doi.org/10.1177/20563051231186368
Rieger ADraws TTheune MTintarev N(2023)Nudges to Mitigate Confirmation Bias during Web Search on Debated Topics: Support vs. ManipulationACM Transactions on the Web10.1145/363503418:2(1-27)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3635034
Zareie ASakellariou R(2023)Maximizing the Diversity of Exposure in Online Social Networks by Identifying Users with Increased Susceptibility to PersuasionACM Transactions on Knowledge Discovery from Data10.1145/362582618:2(1-21)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3625826
Gilmer SBhat AShah SCherry KCheng JGuo J(2023)SUMMIT: Scaffolding Open Source Software Issue Discussion Through SummarizationProceedings of the ACM on Human-Computer Interaction10.1145/36100887:CSCW2(1-27)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610088
Banerjee PChen WLakshmanan L(2023)Mitigating Filter Bubbles Under a Competitive Diffusion ModelProceedings of the ACM on Management of Data10.1145/35893201:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589320
Sun LRao YWu LZhang XLan YNazir A(2023)Fighting False Information from Propagation Process: A SurveyACM Computing Surveys10.1145/356338855:10(1-38)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3563388
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten