Locating targets through mention in Twitter

With the explosive development of social networks, there are excessive amount of user-generated contents available on social media platforms. Indeed, in social networks, it is now a big challenge to promote the right information to the right audiences at the right time. To this end, in this paper, we propose an integrated study of the mention mechanism in social media platforms, such as Twitter, towards locating target audiences for specific information. The study goal is to identify effective targets with high relevance and achieve high response rate as well. Along this line, we formulate the problem of locating targets when posting promotion-oriented messages as a ranking based recommendation task, and present a context-aware recommendation framework as a solution. Specifically, we first extract four categories of features, namely content, social, location and time based features, to measure the relevance among publishers, targets and promotion messages. Then, we employ Ranking Support Vector Machine (SVM) model as the solution to our ranking based recommendation problem. By introducing two bias adjustment parameters, i.e., confidence contributions of publishers and the responsiveness of targets, our framework can effectively recommend top K proper users to mention. Finally, to validate the proposed approach, we conduct extensive experiments on a real world dataset collected from Twitter. The experimental results clearly show that our approach outperforms other baselines with a significant margin.