Sifting micro-blogging stream for events of user interest
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Ranking Approaches for Microblog Search
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
#TwitterSearch: a comparison of microblog search and web search
Proceedings of the fourth ACM international conference on Web search and data mining
Identifying topical authorities in microblogs
Proceedings of the fourth ACM international conference on Web search and data mining
New metric measure for the improvement of search results in microblogs
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
Towards automatic assessment of the social media impact of news content
Proceedings of the 22nd international conference on World Wide Web companion
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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.