Twinder: A search engine for Twitter streams

Abstract

How can one effectively identify relevant messages in the hundreds of millions of Twitter messages that are posted every day? In this paper, we aim to answer this fundamental research question and introduce Twinder, a scalable search engine for Twitter streams. The Twinder search engine exploits various features to estimate the relevance of Twitter messages (tweets) for a given topic. Among these features are both topic-sensitive features such as measures that compute the semantic relatedness between a tweet and a topic as well as topic-insensitive features which characterize a tweet with respect to its syntactical, semantic, sentiment and contextual properties. In our evaluations, we investigate the impact of the different features on retrieval performance. Our results prove the effectiveness of the Twinder search engine - we show that in particular semantic features yield high precision and recall values of more than 35% and 45% respectively. © 2012 Springer-Verlag.