Featured Tweet Search: Modeling Time and Social Influence for Microblog Retrieval

This paper interests in social search over social networking services, typically in microblogging networks. We propose a new approach that integrates, within a Bayesian network model, new relevance factors such as the social importance of microbloggers and the temporal magnitude of tweets. In particular, the social importance of a microblogger is assimilated to his influence on the social network. This property is evaluated by applying PageRank algorithm on the social network of retweets and mentions. The temporal magnitude of microblogs is estimated based on temporal neighbors that present similar query terms. To validate our approach, we conducted a series of experiments on the TREC 2011 Microblog dataset. Results show that the integration of social and temporal features increases the retrieval effectiveness.