BALTIMORE (December 2010) - University of Maryland researchers presented findings from joint research with Community Analytics at the 2010 NIPS Workshop held in British Columbia, Canada. The conference on December 10, 2010 brought together top researchers and practitioners in the field of social computing. Presentations on a broad range of topics pertaining to social computing were presented, ranging everywhere from human computation and social games to trend prediction and dynamics of social networks. Speakers took the floor through invited talks, panel discussion, and oral poster presentations.
The event is organized by a committee of top researchers in the field of computational social science. The goal of the workshop was to overview the state of the art in online advertising, and to discuss future directions and challenges in research and development, from a machine learning point of view. The workshop helped develop a community of researchers who are interested in this field, and will yield future collaboration and exchanges.
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About Community Analytics:
Community Analytics is a unique organization whose singular aim is to connect organizations to the relationships impacting their bottom line. We understand human networks and the roles they play in the decision making process. We combine science, art and a little bit of magic to harness the power of these networks for the greater good: more revenue for you and more relevance for your customers.
About NIPS:
This workshop aims to bring together researchers and practitioners interested in this area to share their perspectives, identify the challenges and opportunities, and discuss future research/application directions through invited talks, panel discussion, and oral/poster presentations.
Computational social science is an emerging academic research area at the intersection of computer science, statistics, and the social sciences, in which quantitative methods and computational tools are used to identify and answer social science questions. The field is driven by new sources of data from the Internet, sensor networks, government databases, crowdsourcing systems, and more, as well as by recent advances in computational modeling, machine learning, statistics, and social network analysis.