YouTube Video Promotion by Cross-Network Association: @Britney to Advertise Gangnam Style

Abstract

The emergence and rapid proliferation of various social media networks have reshaped the way how video contents are generated, distributed, and consumed in traditional video sharing portals. Nowadays, online videos can be accessed from far beyond the internal mechanisms of the video sharing portals, such as internal search and front page highlight. Recent studies have found that external referrers, such as external search engines and other social media websites, arise to be the new and important portals to lead users to online videos. In this paper, we introduce a novel cross-network collaborative application to help drive the online traffic for given videos in the traditional video portal YouTube by leveraging the high propagation efficiency of the popular Twitter followees. Since YouTube videos and Twitter followees distribute on heterogeneous spaces, we present a cross-network association-based solution framework. In this framework, we first represent YouTube videos and Twitter followees in the corresponding topic spaces separately by employing generative topic models. Then, the cross-network topic spaces are associated from both semantic-based and network-based perspectives through the collective intelligence of the observed overlapped users. Based on the derived cross-network association, we finally match the query YouTube videos and candidate Twitter followees in the same topic space with a unified ranking method. The experiments on a real-world large-scale dataset of more than 2.2 million YouTube videos and 31.8 million tweets from 38,540 YouTube users and 39,400 Twitter users demonstrate the effectiveness and superiority of our solution in which network-based and semantic-based association are integrated.