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Research Proposal on Transformer based Opinion Mining Approach for Fake News Detection

Research Proposal on Transformer based Opinion Mining Approach for Fake News Detection

   Social media plays a vital role in spreading fake information due to its low cost, easy access, and rapid dissemination. Fake news detection aims to detect the untrue and fake news propagating with or without intention. Fake news detection is a challenging task due to the increasing complexity that matches real news patterns. Opinion mining plays an important role in detecting fake news.

   Opinion mining helps analyze the sentiments and information manipulation of fake news. Deep learning approaches are highly applied in fake news detection; nonetheless, such approaches produce effective detection. Transformers is an advanced form of deep learning approach that utilizes broad and pre-trained knowledge to improve the effectiveness of the applied model. Transformers for fake news detection exploits rich knowledge information by inferring the context behind the fake news. Incorporating transformers with opinion mining assist in obtaining reliable, robust models for fake news detection.