FakeRecogna Anomaly: Fake News Detection in a New Brazilian Corpus

Abstract

The advances in technology have allowed digital content to be shared in a very short time and reach thousands of people. Fake news is one of the content shared among people and it has a negative impact on our society. Therefore, its detection has become a research topic of great importance in the natural language processing and machine learning communities. Besides the techniques employed for detection, it is also important a good corpus so that machine learning techniques can learn to differentiate between real and fake news. One can find corpora in Brazilian Portuguese; however, they are either outdated or balanced, which does not reflect a real-life situation. This work presents a new updated and imbalanced corpus for the detection of fake news where the detection can be treated as an anomaly detection problem. This work also evaluates the proposed corpus by using classifiers designed for anomaly detection purposes.