Grant: Engage Grants Program

Social media has enabled conversations to occur asynchronously and beyond geographic constraints, but they are still typically bounded by a reasonably well-defined group of participants in some sort of shared social context. Network-driven genres (e.g., social network sites, microblogging) complicate this because people follow the conversations in the context of individuals, not topical threads. Yet, conversations still emerge between dyads and among groups. Online social networks have attracted the interest of millions of users. Facebook has more than 400 million users while Twitter has more than 40 million users (as of July 2009) that exchange over 50 million tweets per day. Users are able to interact with each other, chat, share thoughts and links, play games and conduct several other activities. The popularity of social networking has also attracted the interest of the research community that tries to understand their structure and user interconnection as well as interactions among users. One of the distinguishing features of online social networks and social media is their potential for information propagation. It has been studied both empirically and theoretically for many years by sociologists, statisticians, and computer scientists. In this project, we will use statistical tools and mathematical analysis to answer the following questions: When are people most likely to comment or like a post on a wall in Facebook? When are people most likely to retweet content? Is there a language pattern between content receiving more tweets, likes or comments? Which region is more likely to leave comments? At what time is a specific domain, or social network website the most active? At what time in a specific region is it most active? This information will prove invaluable to our