Applying Hidden Topics in Ranking Social Update Streams on Twitter

As the number of users using Twitter(1) increases, an user may have a lot of friends whose tweet (posting) list (also called as "social update stream" [5, 8, 18]) may overwhelm his/her homepage. This can lead to the situation where important tweets (i.e. the tweets the user is interested in) are pushed down on the list, thus, it takes time to find them. Social update stream ranking is a possible solution that puts important tweets on the top of the page, so that the user can easily read it. In this paper, we propose to apply hidden topics [1, 15, 20] in the Combined Regression Ranking algorithm [2] to rank social update streams. The proposed system works like a content based recommendation system. The experimental results show a significant improvement proving that our proposal is a suitable direction.