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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

TwitterNews+: A Framework for Real Time Event Detection from the Twitter Data Stream

verfasst von : Mahmud Hasan, Mehmet A. Orgun, Rolf Schwitter

Erschienen in: Social Informatics

Verlag: Springer International Publishing

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Abstract

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-the-art 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.

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Metadaten
Titel
TwitterNews+: A Framework for Real Time Event Detection from the Twitter Data Stream
verfasst von
Mahmud Hasan
Mehmet A. Orgun
Rolf Schwitter
Copyright-Jahr
2016
Buch
Social Informatics

Print ISBN: 978-3-319-47879-1

Electronic ISBN: 978-3-319-47880-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47880-7_14

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