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The advent of participatory web has enabled information consumers to become information producers via social media. This phe nomenon has attracted researchers of di
erent 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 activeness 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 de- velop a four-step model: community selection, data collection, online be- havior 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.