An Improved FakeBERT for Fake News Detection

Abstract

In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news generated regularly and the unsupervised nature of social media, fake news turns out to be a big problem. Fake news can easily build a false positive or negative perception about a person, or an event. Fake news was also used as a tool by propagandists during the Coronavirus (COVID-19) pandemic. Thus, there is a need to use technology to tag fake news and prevent its dissemination. Previously, different algorithms were designed to detect fake news but without considering the semantic meaning and long sentence dependence. This research work proposes a new approach to the detection of fake news in the context of COVID-19. The suggested approach uses a combination of Bidirectional Encoder Representations from Transformers (BERT) for extracting context meaning from sentences, SVM for pattern identification to detect fake news in a better way from the COVID-19 dataset, and an evolutionary algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute text for Support Vector Machine (SVM) classification. The suggested approach improves accuracy by 5.2 % by removing a certain amount of ambiguity from sentences.