Real-World Behavior Analysis through a Social Media Lens Mohammad-Ali Abbasi†, Sun-Ki Chai‡, Huan Liu†, Kiran Sagoo‡ †Computer Science and Engineering, Arizona State University ‡Department of Sociology, University of Hawai‘i Ali.abbasi@asu.edu,Sunki@hawaii.edu,Huan.liu@asu.edu,sagoo@hawaii.edu Abstract. The advent of participatory web has enabled information consumers to become information producers via social media. This phe- nomenon 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 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. 1 Introduction The advent of participatory web has created user-generated data [1], that leave massive amounts of online “clues” that can be examined to infer the attributes of the individuals who produced data. As it becomes easier and easier to create content in the virtual world, more and more data is generated in various aspects of life for studying user attitudes and behaviors. Sam Gosling in [7] reveals how his team gathers a large amount of information about people without asking any questions but only by examining the work and living places of their subjects. As we can understand people by studying their physical space and belongings, we are now able to investigate users by studying their online activities, postings, and behavior in a virtual space. This method can be a replacement for traditional data collection methods. Among traditional social science data collection techniques, surveys or ex- periments are structured and active, and generating new data is an important part of the process. The researcher defines what s/he needs, designs question- naires or experimental treatments, and collects the data based on the results