A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Eddi: interactive topic-based browsing of social status streams
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
#TwitterSearch: a comparison of microblog search and web search
Proceedings of the fourth ACM international conference on Web search and data mining
Speak little and well: recommending conversations in online social streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Leveraging the semantics of tweets for adaptive faceted search on twitter
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Groundhog day: near-duplicate detection on Twitter
Proceedings of the 22nd international conference on World Wide Web
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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.