DeBERTNeXT: A Multimodal Fake News Detection Framework

With the ease of access and sharing of information on social media platforms, fake news or misinformation has been spreading in different formats, including text, image, audio, and video. Although there have been a lot of approaches to detecting fake news in textual format only, multimodal approaches are less frequent as it is difficult to fully use the information derived from different modalities to achieve high accuracy in a combined format. To tackle these issues, we introduce DeBertNeXT, a multimodal fake news detection model that utilizes textual and visual information from an article for fake news classification. We perform experiments on the immense Fakeddit dataset and two smaller benchmark datasets, Politifact and Gossipcop. Our model outperforms the existing models on the Fakeddit dataset by about 3.80%, Politifact by 2.10% and Gossipcop by 1.00%.