Digital Divide?: Civic Engagement, Information Poverty, and the Internet Worldwide
Digital Divide?: Civic Engagement, Information Poverty, and the Internet Worldwide
Communities in Cyberspace
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Identifying same wavelength groups from twitter: a sentiment based approach
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Measuring user credibility in social media
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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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.