research-article

Modulating video credibility via visualization of quality evaluations

Authors:

Nicholas Diakopoulos,

Irfan EssaAuthors Info & Claims

WICOW '10: Proceedings of the 4th workshop on Information credibility

Pages 75 - 82

https://doi.org/10.1145/1772938.1772953

Published: 27 April 2010 Publication History

Abstract

In this work we develop and evaluate a method for the syndication and visualization of aggregate quality evaluations of informational video. We enable the sharing of knowledge between motivated media watchdogs and a wider population of casual users. We do this by developing simple visual cues which indicate aggregated activity levels and polarity of quality evaluations (i.e. positive / negative) which are presented in-line with videos as they play. In an experiment we show the potential of these visuals to engender constructive changes to the credibility of informational video under some circumstances. We discuss the limitations, and future work associated with this approach toward video credibility modulation.

References

[1]

Adler, B. T. and Alfaro, L. d., A Content-Driven Reputation System for the Wikipedia. in Proceedings of the 16th International World Wide Web Conference (WWW), (2007).

Digital Library

[2]

Adler, B. T., Chatterjee, K., Alfaro, L. d., Faella, M., Pye, I. and Raman, V., Assigning Trust to Wikipedia Content. in In WikiSym 2008: International Symposium on Wikis, (2008).

Digital Library

[3]

boyd, d., Lee, H.-Y., Ramage, D. and Donath, J., Developing Legible Visualizations for Online Social Spaces. in HICSS, (2002).

Digital Library

[4]

Diakopoulos, N. and Essa, I., An Annotation Model for Making Sense of Information Quality in Online Video. in International Conference on the Pragmatic Web, (Uppsala, Sweden, 2008).

Digital Library

[5]

Diakopoulos, N., Goldenberg, S. and Essa, I., Videolyzer: Quality Analysis of Online Informational Video for Bloggers and Journalists. in Conference on Human Factors in Computing Systems (CHI), (Boston, MA, 2009).

Digital Library

[6]

Diakopoulos, N. and Shamma, A., Characterizing Debate Performance via Aggregated Twitter Sentiment. in Conference on Human Factors in Computing Systems (CHI), (Atlanta, GA, 2010).

Digital Library

[7]

Few, S. Information Dashboard Design: The Effective Visual Communication of Data. O'Reilly Media, 2006.

Digital Library

[8]

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

Digital Library

[9]

Fogg, B. J., Prominence-interpretation theory: explaining how people assess credibility online. in CHI '03: CHI '03 extended abstracts on Human factors in computing systems, (2003), ACM, 722--723.

Digital Library

[10]

Fogg, B. J., Marshall, J., Laraki, O., Osipovich, A., Varma, C., Fang, N., Paul, J., Rangnekar, A., Shon, J., Swani, P. and Treinen, M., What makes Web sites credible?: a report on a large quantitative study. in CHI '01: Proceedings of the SIGCHI conference on Human factors in computing systems, (2001), ACM, 61--68.

Digital Library

[11]

Josephson, S. and Holmes, M. E., Clutter of Content? How on-screen enhancements affect how TV viewers scan and what they learn. in Proc. Symposium on Eye Tracking Research & Application, (2006), 155--162.

Digital Library

[12]

Kittur, A., Chi, E. H. and Suh, B., Crowdsourcing User Studies With Mechanical Turk in Proceedings of CHI, (2008), 453--456.

Digital Library

[13]

Kittur, A., Suh, B. and Chi, E. H. Can You Ever Trust a Wiki? Impacting Perceived Trustworthiness in Wikipedia CSCW, San Diego, CA, 2008, 477--480.

Digital Library

[14]

Lampe, C. and Garrett, R. K., It's all News to Me: The Effect of Insturments on Ratings Provision. in Proceedings of HICSS, (2007).

Digital Library

[15]

Lerman, K. Social Information Processing in News Aggregation. IEEE Internet Computing. 16--28.

Digital Library

[16]

Lord, C. G., Ross, L. and Lepper, M. R. Biased Assimilation and Attitude Polarization: The Effects of Prior Theories on Subsequently Considered Evidence. Journal of Personality and Social Psychology, 37 (11). 2098--2109.

[17]

Manjoo, F. True Enough: Learning to Live in a Post-Fact Society. John Wiley & Sons, 2008.

[18]

McCroskey, J. The effects of evidence in persuasive communication. The Journal of Western Speech Communications, 31. 189--199.

[19]

Miller, K. Communication Theories: Perspectives, Processes, and Contexts. McGraw-Hill, 2004.

[20]

Murakami, K., Nichols, E., Matsuyoshi, S., Sumida, A., Masuda, S., Unui, K. and Matsumoto, Y., Statement Map: Assisting Information Credibility Analysis by Visualizing Arguments. in Workshop on Information Credibility on the Web, (2009).

Digital Library

[21]

Nakamura, S., Shimizu, M. and Tanaka, K., Can Social Annotation Support Users in Evaluating the Trustworthiness of Video Clips? in Workshop on Information Credibility on the Web (WICOW), (2008), 59--62.

Digital Library

[22]

Pentland, A. S. Honest Signals: How They Shape our World. MIT Press, 2008.

Digital Library

[23]

Perry, D. K. Theory and Reserach in Mass Communication: Contexts and Consequences (2nd Edition). Lawrence Erlbaum Associates, 2002.

[24]

Pirolli, P., Wollny, E. and Suh, B., So You Know You're Getting the Best Possible Information: A Tool that Increases Wikipedia Credibility. in Proc. CHI, (2009), 1505--1508.

Digital Library

[25]

Ross, J., Irani, L., Silberman, M. S., Zaldivar, A. and Tomlinson, B., Who are the Crowdworkers? Shifting Demographics in Mechanical Turk. in Conference on Human Factors in Computing Systems (CHI) alt.chi, (2010).

Digital Library

[26]

Shamma, A., Kennedy, L. and Churchill, E., Tweet the Debates. in ACM Multimedia Workshop on Social Media (WSM), (2009).

Digital Library

[27]

Sheng, V. S., Provost, F. and Ipeirotis, P. G., Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers. in KDD, (2008).

Digital Library

[28]

Smith, M. A. and Fiore, A. T., Visualization Components for Persistent Conversations. in Proc CHI, (2001), 136--143.

Digital Library

[29]

Snow, R., O'Connor, B., Jurafsky, D. and Ng, A. Y. Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks Conference on Empirical Methods in Natural Language Processing, 2008.

Digital Library

[30]

Stvilia, B., Twidale, M., Smith, L. and Gasser, L. Information Quality Work Organization in Wikipedia. Journal of the American Society for Information Science and Technology, 59 (6). 983--1001.

Digital Library

[31]

Suh, B., Chi, E. H., Kittur, A. and Pendleton, B. A., Lifting the Veil: Improving Accountability and Social Transparency in Wikipedia with WikiDashboard. in Proc. CHI, (2008), 1037--1040.

Digital Library

[32]

Wyer, R. S., Jr., and Albarracín, D. Belief formation, organization, and change: Cognitive and motivational influences. in D. Albarracín, B. T. Johnson and Zanna, M. P. eds. The handbook of attitudes, Erlbaum, 2005.

Cited By

Yamamoto YShimada S(2017)Analysis on Effect of Disputed Topic Suggestion for User Behavior in Web SearchTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.WII-L32:1(WII-L_1-12)Online publication date: 2017
https://doi.org/10.1527/tjsai.WII-L
Yamamoto YShimada SBlustein JHerder ERubart JAshman H(2016)Can Disputed Topic Suggestion Enhance User Consideration of Information Credibility in Web Search?Proceedings of the 27th ACM Conference on Hypertext and Social Media10.1145/2914586.2914592(169-177)Online publication date: 10-Jul-2016
https://dl.acm.org/doi/10.1145/2914586.2914592
Ginsca APopescu ALupu M(2015)Credibility in Information RetrievalFoundations and Trends in Information Retrieval10.1561/15000000469:5(355-475)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1561/1500000046

Index Terms

Modulating video credibility via visualization of quality evaluations
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools

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Published In

cover image ACM Conferences

WICOW '10: Proceedings of the 4th workshop on Information credibility

April 2010

92 pages

ISBN:9781605589404

DOI:10.1145/1772938

General Chairs:
Katsumi Tanaka
Kyoto University, Japan
,
Xiaofang Zhou
University of Queensland, Australia
,
Min Zhang
Tsinghua University, China
,
Adam Jatowt
Kyoto University, Japan

Copyright © 2010 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]

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Publication History

Published: 27 April 2010

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WWW '10

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ACM

WWW '10: The 19th International World Wide Web Conference

April 27, 2010

North Carolina, Raleigh, USA

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Overall Acceptance Rate 9 of 19 submissions, 47%

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Cited By

Yamamoto YShimada S(2017)Analysis on Effect of Disputed Topic Suggestion for User Behavior in Web SearchTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.WII-L32:1(WII-L_1-12)Online publication date: 2017
https://doi.org/10.1527/tjsai.WII-L
Yamamoto YShimada SBlustein JHerder ERubart JAshman H(2016)Can Disputed Topic Suggestion Enhance User Consideration of Information Credibility in Web Search?Proceedings of the 27th ACM Conference on Hypertext and Social Media10.1145/2914586.2914592(169-177)Online publication date: 10-Jul-2016
https://dl.acm.org/doi/10.1145/2914586.2914592
Ginsca APopescu ALupu M(2015)Credibility in Information RetrievalFoundations and Trends in Information Retrieval10.1561/15000000469:5(355-475)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1561/1500000046

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