Uprising microblogs

Abstract

We investigate in this paper the problem of accessing to real-time information and we propose a Bayesian network retrieval model for tweet search. The proposed model interprets tweet relevance as a conditional probability and estimates it using different sources of evidence. In particular, we introduce a social search model that considers, in addition to text similarity measures, the microblogger's influence, the time magnitude and the presence of hashtags. To evaluate our model, we conducted a series of experiments on the TREC Tweets2011 corpus. Experiments with "Arab Spring" topic set show that both of social and temporal features improve tweet search for different types of queries. Final results show also that our model outperforms other traditional information retrieval baselines.