2020
Authors
Guimaraes, N; Miranda, F; Figueira, A;
Publication
INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING
Abstract
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.
2020
Authors
Cunha, E; Figueira, A;
Publication
Trends and Innovations in Information Systems and Technologies - Advances in Intelligent Systems and Computing
Abstract
2020
Authors
Guimaraes, N; Figueira, A; Torgo, L;
Publication
Communications in Computer and Information Science - Knowledge Discovery, Knowledge Engineering and Knowledge Management
Abstract
2020
Authors
Figueira, A;
Publication
EDULEARN20 Proceedings
Abstract
2020
Authors
Figueira, A;
Publication
EDULEARN20 Proceedings
Abstract
2020
Authors
Guimaraes, N; Figueira, A; Torgo, L;
Publication
Proceedings of the 16th International Conference on Web Information Systems and Technologies
Abstract
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.