Large-scale content management and retrieval for social media

With online social network and mobile devices flourishing, social media is becoming ubiquitous in our daily lives. However, the poor data quality and the diversity of multimedia content are major obstacles to finding desired information for the users. Furthermore, efficient retrieval becomes a non-trivial task due to the massive, and growing amount of media data. Nowadays, social media systems require data cleaning and understanding techniques for intelligent management, as well as efficient and accurate retrieval frameworks to handle the large amount of data. In this thesis, we study different content management and retrieval methodologies to address the above problems.The first part of this thesis aims to improve the data quality of social media by automatically completing the missing information. Specifically, we present a spatial-aware multimodal location estimation framework to predict the unknown location for some media data. Our method consists of multiple models which utilize various information sources, including textual and visual content of the social media.