Framework for the Discovery of Newsworthy Events in Social Media | IGI Global Scientific Publishing
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Framework for the Discovery of Newsworthy Events in Social Media

Framework for the Discovery of Newsworthy Events in Social Media

Fernando José Fradique Duarte (University of Aveiro, Aveiro, Portugal), Óscar Mortágua Pereira (Instituto de Telecomunicações, DETI – University of Aveiro, Aveiro, Portugal), and Rui L. Aguiar (Instituto de Telecomunicações, DETI – University of Aveiro, Aveiro, Portugal)
Copyright: © 2019 |Volume: 9 |Issue: 3 |Pages: 18
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781522566120|DOI: 10.4018/IJOCI.2019070103
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MLA

Duarte, Fernando José Fradique, et al. "Framework for the Discovery of Newsworthy Events in Social Media." IJOCI vol.9, no.3 2019: pp.45-62. https://doi.org/10.4018/IJOCI.2019070103

APA

Duarte, F. J., Pereira, Ó. M., & Aguiar, R. L. (2019). Framework for the Discovery of Newsworthy Events in Social Media. International Journal of Organizational and Collective Intelligence (IJOCI), 9(3), 45-62. https://doi.org/10.4018/IJOCI.2019070103

Chicago

Duarte, Fernando José Fradique, Óscar Mortágua Pereira, and Rui L. Aguiar. "Framework for the Discovery of Newsworthy Events in Social Media," International Journal of Organizational and Collective Intelligence (IJOCI) 9, no.3: 45-62. https://doi.org/10.4018/IJOCI.2019070103

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Abstract

The new communication paradigm established by social media along with its growing popularity in recent years contributed to attract an increasing interest of several research fields. One such research field is the field of event detection in social media. The contribution of this article is to implement a system to detect newsworthy events in Twitter. The proposed pipeline first splits the tweets into segments. These segments are then ranked. The top k segments in this ranking are then grouped together. Finally, the resulting candidate events are filtered in order to retain only those related to real-world newsworthy events. The implemented system was tested with three months of data, representing a total of 4,770,636 tweets written in Portuguese. In terms of performance, the proposed approach achieved an overall precision of 88% and a recall of 38%.

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