Social Profiling of Flickr: Integrating Multiple Types of Features for Gender Classification
Mohammed Ali Eltaher, Jeongkyu Lee · University of Bridgeport ScholarWorks (University of Bridgeport) · 2015
With the pervasive use of social media sites, an extraordinary amount of data has been generated in different data types such as text and image. Combining image features and text information annotated by users reveals interesting properties of social user mining, and serves as a powerful way of discovering unknown information about the users. However, there has been few research work reported about combination of image and text data for social user mining. In this study, we propose a novel idea to classify the gender of user by integrating multiple types of features. We utilize not only text information, i.e., tag or description, but also images posted by a user with semantic based data fusion technique.