Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Bloggers as experts: feed distillation using expert retrieval models
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Telling experts from spammers: expertise ranking in folksonomies
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
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
Identifying topical authorities in microblogs
Proceedings of the fourth ACM international conference on Web search and data mining
Topic-driven multi-type citation network analysis
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Uprising microblogs: a bayesian network retrieval model for tweet search
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Hybrid Method for Computing Word-Pair Similarity based on Web Content
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
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In recent years, microblogging services like Twitter, draws the attention of users. These micro-blogs attract more and more users due to the ease and the speed of information sharing especially in real time. Microbloggers, while posting microblogs, search for fresh information related to their interests. Finding good results concerning the given subjects needs to consider the features of microblogs. Several works have proposed criteria for tweets search, but, this area is still not well exploited, consequently, search results are irrelevant. In this paper, we propose new features (for example audience and RetweetRank). We investigate the impact of these criteria on the search's results for relevant information. Finally, we propose a new metric to improve the results of the searches in microblogs. More accurately, we propose a research model that combines content relevance, tweet relevance and author relevance. Each type of relevance is characterized by a set of criteria such as audience to assess the relevance of the author, OOV (Out Of Vobulary) to measure the relevance of content and others. To evaluate our model, we used a corpus of subjective tweets talking about Tunisian actualities in 2012.