Social TV is a term that broadly describes the online social interactions that occur between viewers while watching television. Television shows are now instigating online social interactions between viewers by requesting viewers, as part of the first broadcasts, to participate in simultaneous discussions about the shows. Examples include asking users to follow and comment on topics on Twitter. A more passive approach is also used that displays messages from social media participants on the TV screen during broadcasts. As a result of this trend of encouraging people to participate during TV viewing, social TV applications have arisen to facilitate the discussions even further. In this keynote I will discuss how TV networks and advertisers can derive value from all of the online social activity about TV.
Shawndra Hill is an Assistant Professor in Operations and Information Management at the Wharton School of the University of Pennsylvania. Generally, she studies data mining, machine learning and statistical relational learning and their alignment with business problems. Specifically, she researches the value to companies of mining data on how consumers interact with each other — for targeted marketing, advertising and fraud detection. Here current research focusses on the interactions between TV content and Social Media. Her past and present industry partners include AT&T Labs Research, ClearForest, and Siemens Energy & Automation. Her recent work appears in IEEE Transactions on Data and Knowledge Engineering, Journal of Computational and Graphical Statistics, SIGKDD Explorations, and Statistical Science. Her research is funded in part by the Office of Naval Research, Google, and the National Institutes of Health (NIH). Shawndra holds a B.S. in Mathematics from Spelman College, a B.E.E. from the Georgia Institute of Technology and a Ph.D. in Information Systems from NYU’s Stern School of Business.
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