MalFake: A Multimodal Fake News Identification for Malayalam using Recurrent Neural Networks and VGG-16
This addresses the problem of fake news spreading in regional languages like Malayalam for Indian users, but it is incremental as it applies existing multimodal methods to a new language context.
The paper tackles fake news detection in Malayalam by developing a multimodal deep learning model combining Recurrent Neural Networks and VGG-16, achieving improved accuracy over unimodal approaches, though no specific numbers are provided.
The amount of news being consumed online has substantially expanded in recent years. Fake news has become increasingly common, especially in regional languages like Malayalam, due to the rapid publication and lack of editorial standards on some online sites. Fake news may have a terrible effect on society, causing people to make bad judgments, lose faith in authorities, and even engage in violent behavior. When we take into the context of India, there are many regional languages, and fake news is spreading in every language. Therefore, providing efficient techniques for identifying false information in regional tongues is crucial. Until now, little to no work has been done in Malayalam, extracting features from multiple modalities to classify fake news. Multimodal approaches are more accurate in detecting fake news, as features from multiple modalities are extracted to build the deep learning classification model. As far as we know, this is the first piece of work in Malayalam that uses multimodal deep learning to tackle false information. Models trained with more than one modality typically outperform models taught with only one modality. Our study in the Malayalam language utilizing multimodal deep learning is a significant step toward more effective misinformation detection and mitigation.