A Transformer-Based Approach to Understand and Identify The Fake News In Bangla Media

The rapid growth in the digital media in Bangladesh has increased the spread of misinformation in online news portals and social networks. Detection of fake news in Bengali is a special problem because of the complexity of the language, informative manifestations and often a combination of the textual presentation of persuasion and false visual information. The proposed paper suggests a transfer learning-based multimodal model that incorporates BanglaBERT to provide text contextual representation and ResNet50 to obtain visual features. The features obtained are pooled in one representation and then subject to fully connected layers to perform binary classification. The model is trained on the AdamW optimizer and is tested on stratified data splits and cross validation. As the experimental results illustrate, the suggested system is 92.81% accurate, which is higher than the text-only and image-only baselines. The results prove that multimodal learning is effective in detecting fake news in Bangali and stress that it can be used in the real-life.