MalFake: A Multimodal Fake News Identification for Malayalam using
  Recurrent Neural Networks and VGG-16
Adhish S. Sujan Ajitha. V Aleena Benny Amiya M. P. V. S. Anoop
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.