Date of Award

2023

Publication Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Fakeddit dataset, DeBERTa, Fake news, Multi-modal, Transfer learning

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

There is a rapid influx of fake news nowadays, which poses an immense threat to our society. Fake news has been impacting us in several ways which include changing our thoughts, manipulating opinions, and also causing chaos due to misinformation. With the ease of access and sharing information on social media platforms, such fake news or misinformation has been spreading in different modalities which include text, image, audio, and video. Although there have been a lot of approaches to detecting fake news in textual format only, however, 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 which is a multimodal fake news detection model that utilizes both textual and visual information from an article for fake news classification. We perform experiments on the immense Fakeddit dataset and two other smaller benchmark datasets named 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%.

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