Machine Learning for Social Computing

Social computing aims to support the online social behavior through computational methods. The explosion of the Web has created and been creating social interactions and social contexts through the use of software, services and technologies, such as blogs, microblogs (Tweets), wikis, social network services, social bookmarking, social news, multimedia sharing sites, online auctions, reputation systems, and so on. Analyzing the information underneath the social interactions and social context, e.g., community detection, opinion mining, trend prediction, anomaly detection, product recommendation, expert finding, social ranking, information visualization, will benefit both of information providers and information consumers in the application areas of social sciences, economics, psychologies and computer sciences. However, the large volumes of user-generated contents and the complex structures among users and related entities require effective modeling methods and efficient solving algorithms, which therefore bring challenges to advanced techniques in machine learning. There are three major concerns:

1. How to effectively and accurately model the related task as a learning problem?
2. How to construct efficient and scalable algorithm to solve the learning task?
3. How to fully explore and exploit human computation?


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.


We invite papers solving the problems in social computing using machine learning methods, such as statistical methods, graphical models, graph mining methods, matrix factorization, learning to rank, optimization, temporal analysis methods, information visualization methods, transfer learning, and others.