Social Sharing Platforms, a great source of free and diverse information, have been center of attraction to many people. Users post their opinions, thoughts, life events, news and all other information. This data flowed into these systems has increased to such a limit making nearly impossible for a user to read all or even most of it, analyzing and utilize it. As a solution to this problem, we here are proposing an approach, which makes use of not only the tweets themselves but also their properties to re-rank the tweets given a user query. The proposed approach was implemented in a prototype system and test results were generated. A set of feedback data collected via online survey for those test results provides a good evaluation score, with an average improvement of around 10% on precision values after removing the outliers. It shows that our approach can generate improved results over the original ones.