skip to main content
10.1145/2492517.2492574acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Determining credibility from social network structure

Published:25 August 2013Publication History

ABSTRACT

The increasing proliferation of social media results in users that are forced to ascertain the truthfulness of information that they encounter from unknown sources using a variety of indicators (e.g. explicit ratings, profile information, etc.). Through human-subject experimentation with an online social network-style platform, our study focuses on the determination of credibility in ego-centric networks based on subjects observing social network properties such as degree centrality and geodesic distance. Using manipulated social network graphs, we find that corroboration and degree centrality are most utilized by subjects as indicators of credibility. We discuss the implications of the use of social network graph structural properties and use principal components analysis to visualize the reduced dimensional space.

References

  1. M. Granovetter, "The Strength of Weak Ties: A Network Theory Revisited," in Sociological Theory vol. 1, 1983, pp. 201--233.Google ScholarGoogle ScholarCross RefCross Ref
  2. D. Easley and J. Kleinberg Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. E. Gilbert & K. Karahalios, "Predicting tie strength with social media." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Antin and E. F. Churchill, "Badges in social media: A social psychological perspective." in CHI 2011. Vancouver, BC, Canada. May 7--12, 2011.Google ScholarGoogle Scholar
  5. V. L. Rubin and E. D. Liddy, "Assessing credibility of weblogs."AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW). ACM, 2006.Google ScholarGoogle Scholar
  6. T. Y. Berger-Wolf and J. Saia, "A framework for analysis of dynamic social networks." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C. L. Armstrong and M. J. McAdams, "Blogs of information: How gender cues and individual motivations influence perceptions of credibility."Journal of Computer-Mediated Communication vol. 14 iss. 3, 2009, pp. 435--456.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. Lesani and N. Montazeri, "Fuzzy Trust Aggregation and Personalized Trust Inference in Virtual Social Networks," Computational Intelligence vol. 25 iss. 2, 2009, pp. 51--83.Google ScholarGoogle ScholarCross RefCross Ref
  9. Y. Kim and H. Song, "Strategies for predicting local trust based on trust propagation in social networks," In Knowledge-Based Systems vol. 2, 2011, pp. 1360--1371.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. R. Guha, R. Kumar, R. Raghavan and A. Tomkins, "Propagation of trust and distrust," Proceedings of the 13th International Conference on World Wide Web. New York, NY. 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. E. Brashears, "Humans use Compression Heuristics to Improve the Recall of Social Networks," in Scientific reports, vol. 3, 2013. doi: 10.1038/srep01513Google ScholarGoogle Scholar
  12. R. Dunbar, "How many friends does one person need?: Dunbar's number and other evolutionary quirks," London: Faber and Faber, 2010.Google ScholarGoogle Scholar
  13. P. Resnick, R. Zeckhauser, J. Swanson, and & K. Lockwood, "The Value of Reputation on e-Bay: A Controlled Experiment," Experimental Economics, vol. 2(9), 2006.Google ScholarGoogle Scholar
  14. L. Page, S. Brin, R. Motwani, and T. Winograd, "The PageRank Citation Ranking: Bringing Order to the Web, Stanford Digital Library Technologies Project, CA. Technical report, 1998.Google ScholarGoogle Scholar
  15. J. A. Golbeck and J. Hendler, "FilmTrust: Movie recommendations using trust in Web-based social networks," In Proceedings of the IEEE Consumer Communications and Networking Conference. Las Vegas, NV, 2006.Google ScholarGoogle Scholar
  16. J. A. Golbeck, "Computing and applying trust in web-based social networks," Ph.D. dissertation, Dept. of Computer Science, Univ. of Maryland., College Park, MD, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter." In Proceedings of the 20th International Conference on World Wide Web. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. C. J. Hutto, E. Gilbert, and S. Yardi, "A Longitudinal Study of Follow Predictors on Twitter," in Proceedings of the 2013 ACM Annual Conference on Human Factors in Computing Systems (CHI). Paris, France, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. M. Romero, W. Galuba, S. Asur, and B. A. Huberman, "Influence and passivity in social media," in Machine learning and knowledge discovery in databases, pp. 18--33. Springer Berlin Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. G. A. Janicik and R. P. Larrick, "Social Network Schemas and The Learning of Incomplete Networks." In Journal of Personality and Social Psychology vol. 88, 2005, pp. 348--364.Google ScholarGoogle ScholarCross RefCross Ref
  21. N. Luhmann "Familiarity, confidence, trust: Problems and alternatives." in Gambetta, Diego (ed.) Trust: Making and Breaking Cooperative Relations, electronic edition, Department of Sociology, University of Oxford, chapter 6, pp. Trust: Making and breaking cooperative relations, 6, 2000, pp. 94--107.Google ScholarGoogle Scholar
  22. B. M. DePaulo, J. J. Lindsay, B. E. Malone, L. Muhlenbruck, K. Charlton, and H. Cooper, "Cues to deception," Psychological bulletin, vol. 129(1), 2003, p. 74.Google ScholarGoogle ScholarCross RefCross Ref
  23. L. Zhou, J. K. Burgoon, J. F. Nunamaker, and D. Twitchell, "Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communications," in Group decision and negotiation, vol. 13(1), 2004, pp. 81--106.Google ScholarGoogle Scholar
  24. L. Weiss, E. Briscoe, H. Hayes, O. Kemenova, S. Harbert, L. Li, G. Lebanon, C. Stewart, D. Miller, and D. Foy. "A Comparative Study of Social Media and Traditional Polling in the Egyptian Uprising of 2011," in Social Computing, Behavioral-Cultural Modeling and Prediction Lecture Notes in Computer Science, vol. 7812, 2013, pp 303--310. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

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

Sign in
  • Published in

    cover image ACM Conferences
    ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
    August 2013
    1558 pages
    ISBN:9781450322409
    DOI:10.1145/2492517

    Copyright © 2013 Copyright is held by the owner/author(s)

    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 25 August 2013

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    Overall Acceptance Rate116of549submissions,21%

    Upcoming Conference

    KDD '24

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader