Keywords attention for fake news detection using few positive labels

Autor(es)

de Souza, Mariana Caravanti ; Golo, Marcos Paulo Silva ; Jorge, Alipio Mario Guedes ; de Amorim, Evelin Carvalho Freire ; Campos, Ricardo Nuno Taborda ; Marcacini, Ricardo Marcondes ; Rezende, Solange Oliveira

• Tipo de documento: Artigo Científico
• Fonte: INFORMATION SCIENCES; v. 663, p. 23-pg., 2024-02-14.

Resumo

Fake news detection (FND) tools are essential to increase the reliability of information in social media. FND can be approached as a machine learning classification problem so that discriminative features can be automatically extracted. However, this requires a large news set, which in turn implies a considerable amount of human experts' effort for labeling. In this paper, we explore Positive and Unlabeled Learning (PUL) to reduce the labeling cost. In particular, we improve PUL with the network-based Label Propagation (PU-LP) algorithm. PU-LP achieved competitive results in FND exploiting relations between news and terms and using few labeled fake news. We propose integrating an attention mechanism in PU-LP that can define which terms in the network are more relevant for detecting fake news. We use GNEE, a state-of-the-art algorithm based on graph attention networks. Our proposal outperforms state-of-the-art methods, improving F-1 in 2% to 10%, especially when only 10% labeled fake news are available. It is competitive with the binary baseline, even when nearly half of the data is labeled. Discrimination ability is also visualized through t-SNE. We also present an analysis of the limitations of our approach according to the type of text found in each dataset. (AU)

• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia
• Processo FAPESP: 19/25010-5 - Representações semanticamente enriquecidas para mineração de textos em português: modelos e aplicações
• Beneficiário: Solange Oliveira Rezende
• Modalidade de apoio: Auxílio à Pesquisa - Regular