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.
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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.
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).
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.
Burke, M., Adamic, L. A., & Marciniak, K. (2013). Families on facebook. In Proceedings of ICWSM 2013.
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.
Tchokni, S., Séaghdha, D. O., & Quercia, D. (2014). Emoticons and phrases: Status symbols in social media.
De Choudhury, M., & De, S. (2014). Mental health discourse on reddit: Self-disclosure, social support, and anonymity.
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.
Acemoglu, D., & Ozdaglar, A. (2011). Opinion dynamics and learning in social networks. Dynamic Games and Applications, 1(1), 3–49.
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.
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.
Weng, J., & Lee, B. S. (2011). Event detection in twitter. In ICWSM.
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.
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Roy, S.D., Zeng, W. (2015). Revelations from Social Multimedia Data. In: Social Multimedia Signals. Springer, Cham. https://doi.org/10.1007/978-3-319-09117-4_10
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DOI: https://doi.org/10.1007/978-3-319-09117-4_10
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