Real-World Behavior Analysis through a Social Media Lens
The advent of participatory web has enabled information consumers to become information producers via social media. This phenomenon has attracted researchers of different disciplines including social scientists, political parties, and market researchers to study social media as a source of data to explain human behavior in the physical world. Could the traditional approaches of studying social behaviors such as surveys be complemented by computational studies that use massive user-generated data in social media? In this paper, using a large amount of data collected from Twitter, the blogosphere, social networks, and news sources, we perform preliminary research to investigate if human behavior in the real world can be understood by analyzing social media data. The goals of this research is twofold: (1) determining the relative effectiveness of a social media lens in analyzing and predicting real-world collective behavior, and (2) exploring the domains and situations under which social media can be a predictor for real-world’s behavior. We develop a four-step model: community selection, data collection, online behavior analysis, and behavior prediction. The results of this study show that in most cases social media is a good tool for estimating attitudes and further research is needed for predicting social behavior.
KeywordsSocial Medium Arab World Word Category Twitter User Online Behavior
Unable to display preview. Download preview PDF.
- 1.Agarwal, N., Liu, H., Tang, L., Yu, P.: Identifying the influential bloggers in a community. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 207–218. ACM (2008)Google Scholar
- 2.Asur, S., Huberman, B.A.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 492–499. IEEE (2010)Google Scholar
- 3.Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science (2011)Google Scholar
- 5.Chai, S.: Choosing an identity: A general model of preference and belief formation. Univ. of Michigan Pr. (2001)Google Scholar
- 7.Gosling, S., Drummond, D., NetLibrary, I.: Snoop: What your stuff says about you. BBC Audiobooks America (2008)Google Scholar
- 9.Norris, P.: Digital divide: Civic engagement, information poverty, and the Internet worldwide. Cambridge Univ. Pr. (2001)Google Scholar
- 10.OConnor, B., Balasubramanyan, R., Routledge, B., Smith, N.: From Tweets to polls: Linking text sentiment to public opinion time series. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, pp. 122–129 (2010)Google Scholar
- 11.Parsons, T., Shils, E., Smelser, N.J.: Toward a general theory of action: Theoretical foundations for the social sciences. Transaction Pub. (2001)Google Scholar
- 12.Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieva (2007)Google Scholar
- 13.Smith, M., Kollock, P.: Communities in cyberspace. Psychology Press (1999)Google Scholar
- 14.Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, ACL 2002 (2002)Google Scholar