Roney Santos


Measuring the Impact of Readability Features in Fake News Detection
Roney Santos | Gabriela Pedro | Sidney Leal | Oto Vale | Thiago Pardo | Kalina Bontcheva | Carolina Scarton
Proceedings of the Twelfth Language Resources and Evaluation Conference

The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.