Combining compact news representations generated using DistilBERT and topological features to classify fake news

• Carlos Abel Córdova Sáenz Universidade Federal do Rio Grande do Sul
• Marcelo Dias Universidade Federal do Rio Grande do Sul, Instituto Federal de Educação Ciência e Tecnologia Sul-rio-grandense
• Karin Becker Universidade Federal do Rio Grande do Sul

DOI: https://doi.org/10.5753/kdmile.2020.11978

Resumo

Fake news (FN) have affected people’s lives in unimaginable ways. The automatic classification of FN is a vital tool to prevent their dissemination and support fact-checking. Related work has shown that FN spread faster, deeper, and more broadly than the truth on social media. Besides, deep learning has produced state-of-the-art solutions in this field, mainly based on textual attributes. In this paper, we propose initial experiments to combine compact representations of the textual news properties generated using DistilBERT, with topological metrics extracted from the social propagation network. Using a dataset related to politics and five distinct classification algorithms, our results are encouraging. Regarding the textual attributes, we reached results comparable to state-of-the-art solutions using only the news title and contents, which is useful for FN early detection. The topological attributes were not as effective, but the promising results encourage the investigation of alternative architectures for their combination

Palavras-chave: distilBERT, fake news, fake news classifification, topological features

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