Cross Social Media Recommendation.

The proliferation of rich social media data revolutionizes the way people perceive and understand the world. Unfortunately, so far, there does not exist a single social media system that efficiently globalizes users around the world. Two well-known social media systems, Twitter and Facebook, are strictly blocked in mainland China for political reasons, which means 21.97% of Internet users are excluded from these systems. Similarly, the second-largest microblogging system in the world, Sina Weibo, features a default system language of Chinese, which rules out many users from other countries. This creates what we call language, network, and culture bubbles. As a result, if we are interested in modeling the knowledge of the world, all research findings based on a single social media system (within a bubble) can be biased, and the social networks or knowledge networks generated from a single system or social community cannot fully represent people from around the world. In this study, we generate a pseudo-social heterogeneous network - Pseudo Global Social Media Network (PGSMN), which bridges the topics of Twitter and Weibo. On this network, all Weibo and Twitter nodes are interconnected via an interim knowledge layer, and user or topic nodes from Twitter can randomly walk to the nodes on Weibo (via different kinds of paths), and vice versa, which enables cross-network information recommendation and knowledge globalization.