New metric measure for the improvement of search results in microblogs

Abstract

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.