DistilBERT and RoBERTa Models for Identification of Fake News

Aleksandar Kitanovski, Martina Toshevska, Georgina Mirceva

Abstract: The proliferation of fake news has become a significant issue in today’s society, affecting the public’s perception of current events and causing harm to individuals and organizations. Therefore, the need for automated systems that can identify and flag fake news is critical. This paper presents a study on the effectiveness of DistilBERT and RoBERTa, two state-of-the-art language models, for detecting fake news. In this study, we trained both models on a dataset of labelled news articles and evaluated them on two different datasets, comparing their performance in terms of accuracy, precision, recall and F1-score. The results of our experiments show that both models perform well in detecting fake news, with RoBERTa model achieving slightly better results in overall. Our study highlights the ability of these models to effectively identify fake news and help combat misinformation.

External IDs:dblp:conf/mipro/KitanovskiTM23