Detalhes
Nome
Nuno Ricardo GuimarãesCluster
InformáticaCargo
Investigador AuxiliarDesde
01 dezembro 2015
Nacionalidade
PortugalCentro
Laboratório de Inteligência Artificial e Apoio à DecisãoContactos
+351220402963
nuno.r.guimaraes@inesctec.pt
2021
Autores
Guimaraes, N; Figueira, A; Torgo, L;
Publicação
PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021)
Abstract
2021
Autores
Guimarães, N; Figueira, A; Torgo, L;
Publicação
Online Soc. Networks Media
Abstract
2021
Autores
Guimaraes, N; Figueira, A; Torgo, L;
Publicação
MATHEMATICS
Abstract
The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.
2021
Autores
Rodrigues, T; Guimaraes, N; Monteiro, J;
Publicação
EUROPEAN PSYCHIATRY
Abstract
2020
Autores
Guimaraes, N; Miranda, F; Figueira, A;
Publicação
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.
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