Toward precise user-topic alignment in online social media

Mining users´= topics of interest is one of the most important tasks for social media services. Given known topic associations for some fraction of the users in an online microblogging platform, our goal is to infer the topics of interest for the remaining users in the same site. Specifically, we proposed a novel bi-relational graph model to capture the interactions among users and their shared topics of interests. The proposed graph model contains two sub-graphs: one corresponds to users and the other corresponds to topics. Such a representation allows for effective exploitation of both user homophily relation and topic correlation simultaneously. This is in contrast with previous work where these two factors are considered in isolation. Subsequently, the user interest discovery problem is formulated as a multi-label learning problem on the bi-relational graph, with the goal to estimate the optimized associations between user nodes and topic nodes across the two sub-graphs. Our experiment is carried out with a complete month-long data collected from Twitter and Tumblr via GNIP Decahose¹ and Firehose² respectively. The large-scale studies shed light on the effectiveness of inferring user interests based on the underlying social connections.