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Title: Framework for the Discovery of Newsworthy Events in Social Media
Author: Duarte, Fernando José Fradique
Pereira, Óscar Mortágua
Aguiar, Rui L.
Keywords: Directed Acyclic Graph
Dynamic Programming
Event Detection
Jarvis-Patrick Clustering
KNN Neighbors
Machine XGBoost
Naïve Bayes
Random Forest
Issue Date: 1-Jul-2019
Publisher: IGI Global
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 realworld 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%.
Peer review: yes
DOI: 10.4018/IJOCI.2019070103
ISSN: 1947-9344
Appears in Collections:DETI - Artigos

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