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Revelations from Social Multimedia Data

  • Suman Deb RoyEmail author
  • Wenjun Zeng
Chapter

Abstract

In this chapter, we take a closer look at some of the success stories in harnessing interesting information from social multimedia data. Being essentially a multi-disciplinary field of research, social multimedia draws perspectives from several domains of expertise. Using the social multimedia signals we have seen so far, we can tackle problems in several domains of science, including psychology, social science, journalism etc. Moreover, there are some hidden signals in this data in the humanities domain, including anthropology, cultural habits, linguistics and education.

Keywords

Privacy Preserve Mutual Friend Secure Multiparty Computation Sentiment Polarity Social Multimedia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chen, J., Cypher, A., Drews, C., & Nichols, J. (2013). CrowdE: filtering tweets for direct customer engagements. In Seventh International AAAI Conference on Weblogs and Social Media.Google Scholar
  2. 2.
    Baccianella, S., Esuli, A., & Sebastiani, F. (2010, May). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In LREC (vol. 10, pp. 2200–2204).Google Scholar
  3. 3.
    Lotan, G., Graeff, E., Ananny, M., Gaffney, D., & Pearce, I. (2011). The Arab Spring| the revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions. International Journal of Communication, 5, 31.Google Scholar
  4. 4.
    Burke, M., Adamic, L. A., & Marciniak, K. (2013). Families on facebook. In Proceedings of ICWSM 2013.Google Scholar
  5. 5.
    Silva, T. H., de Melo, P. O., Almeida, J., Musolesi, M., & Loureiro, A. (2014). You are what you eat (and drink): Identifying cultural boundaries by analyzing food & drink habits in foursquare. arXiv preprint arXiv:1404.1009.Google Scholar
  6. 6.
    Tchokni, S., Séaghdha, D. O., & Quercia, D. (2014). Emoticons and phrases: Status symbols in social media.Google Scholar
  7. 7.
    De Choudhury, M., & De, S. (2014). Mental health discourse on reddit: Self-disclosure, social support, and anonymity.Google Scholar
  8. 8.
    Krishnamurthy, V., and Poor, H. V. (2013). Social learning and bayesian games in multiagent signal processing: How do local and global decision makers interact? In IEEE Signal Processing Magazine, vol. 30, no. 3, May 2013.Google Scholar
  9. 9.
    Acemoglu, D., & Ozdaglar, A. (2011). Opinion dynamics and learning in social networks. Dynamic Games and Applications, 1(1), 3–49.CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Wang, Q., Zeng, W., & Tian, J. (2014). Compressive sensing based secure multiparty privacy preserving framework for collaborative data-mining and signal processing. In IEEE International Conference on Multimedia and Expo, July 2014.Google Scholar
  11. 11.
    Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web (pp. 851–860). ACM.Google Scholar
  12. 12.
    Weng, J., & Lee, B. S. (2011). Event detection in twitter. In ICWSM.Google Scholar
  13. 13.
    Hoang, T. A., Lim, E. P., Achananuparp, P., Jiang, J., & Zhu, F. (2011). On modeling virality of twitter content. In Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation (pp. 212–221). Springer Berlin Heidelberg.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.BetaworksNew YorkUSA
  2. 2.Department of Computer ScienceUniversity of MissouriColumbiaUSA

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