Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/26037
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 Learning Machine XGBoost Naïve Bayes Random Forest SVM |
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 |
URI: | http://hdl.handle.net/10773/26037 |
DOI: | 10.4018/IJOCI.2019070103 |
ISSN: | 1947-9344 |
Appears in Collections: | DETI - Artigos |
Files in This Item:
File | Description | Size | Format | |
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(JA) - 2019-07-01 (IJOCI) Framework for the Discovery of Newsworthy Events in Social Media.pdf | 871.51 kB | Adobe PDF | View/Open |
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