Mining Cross-network Association for YouTube Video Promotion

We introduce a novel cross-network collaborative problem in this work: given YouTube videos, to find optimal Twitter followees that can maximize the video promotion on Twitter. Since YouTube videos and Twitter followees distribute on heterogeneous spaces, we present a cross-network association-based solution framework. Three stages are addressed: (1) heterogeneous topic modeling, where YouTube videos and Twitter followees are modeled in topic level; (2) cross-network topic association, where the overlapped users are exploited to conduct cross-network topic distribution transfer; and (3) referrer identification, where the query YouTube video and candidate Twitter followees are matched in the same topic space. Different methods in each stage are designed and compared by qualitative as well as quantitative experiments. Based on the proposed framework, we also discuss the potential applications, extensions, and suggest some principles for future heterogeneous social media utilization and cross-network collaborative applications.