With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a multiple contexts. Because many social interactions currently take place in online networks, social scientists have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, these investigations required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of big data can allow social scientists to study social interactions on a scale and at a level of detail that has never before been possible. Our goal is to evaluate the value of big data in various social applications and build a framework that models the cost/utility of data. By considering important problems such as Trend Analysis, Opinion Change and User Behavior Analysis during major events in online social networks, we demonstrate the significance of this problem. Furthermore, in each case we present scalable techniques and algorithms that can be used in an online manner. Finally, we propose the big data value evaluation framework that weighs in the cost as well as the value of data to determine capacity modeling in the context of data acquisition.