In recent years, substantial research efforts have gone into investigating different approaches to the detection of events in real time from the Twitter data stream. Most of these approaches, however, suffer from a high computational cost and are not evaluated using a publicly available corpus, thus making it difficult to properly compare them. In this paper, we propose a scalable event detection system, TwitterNews+, to detect and track newsworthy events in real time. TwitterNews+ uses a novel approach to cluster event related tweets from Twitter with a significantly lower computational cost compared to the existing state-of-theart approaches. Finally, we evaluate the effectiveness of TwitterNews+ using a publicly available corpus and its associated ground truth data set of newsworthy events. The result of the evaluation shows a significant improvement, in terms of recall and precision, over the baselines we have used.
CITATION STYLE
Hasan, M., Orgun, M. A., & Schwitter, R. (2016). TwitterNews+: A framework for real time event detection from the twitter data stream. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10046 LNCS, pp. 224–239). Springer Verlag. https://doi.org/10.1007/978-3-319-47880-7_14
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