Large-scale cross-media analysis and mining from socially curated contents

Abstract

The major interest of the current social network service (SNS) developers and users are rapidly shifting from conventional text-based (micro)blogs such as Twitter and Facebook to multimedia contents such as Flickr, Snapchat, MySpace and Tumblr. However, the ability to analyze and exploit unorganized multimedia contents on those services still remain inadequate, even with state-of-the-art media processing and machine learning techniques. This paper focuses on another emerging trend called social curation, a human-in-the-loop alternative to automatic algorithms for social media analysis. Social curation can be defined as a spontaneous human process of remixing social media content for the purpose of further consumption. What characterize social curation are definitely the manual efforts involved in organizing a collection of social media contents, which indicates that socially curated content has a potential as a promising information source against automatic summaries generated by algorithms. Curated contents would also provide latent perspectives and contexts that are not explicitly presented in the original resources. Following this trend, this paper presents recent developments and growth of social curation services, and reviews several research trials for cross-media analysis and mining from socially curated contents.