Twinder

Abstract

How can the search process on Twitter be improved to better meet the various information needs of its users? As an answer to this question, we have developed the Twinder framework, a scalable search system for Twitter streams. Twinder contains algorithms to determine the relevance of tweets in relation to search requests, as well as components to detect (near-)duplicate content, to diversify search results, and to personalize the search result ranking. In this paper, we report on our current progress, including the system architecture and the different modules for solving specific problems. Finally, we empirically determine the effectiveness of Twinder's components with experiments on representative datasets.