Identifying journalistically relevant social media texts using human and automatic methodologies

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Guimaraes, N (Author) Other; Miranda, F (Author) Other; Figueira, A (Author) FCUP

Journal

Title: International Journal of Grid and Utility Computing

Vol. 11

Pages: 72-83

ISSN: 1741-847X

Publisher: Inderscience Publishers

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Other information

Authenticus ID: P-00R-FT8

DOI: 10.1504/ijguc.2020.103971

Abstract (EN): Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12