Fake news is false or misleading information shared as real news, often meant to trick or influence public opinion. Its fast spread through social media causes confusion, divides communities, and reduces trust in reliable news sources. While much research has been done on fake news detection in English, there is a lack of tools designed specifically for Bangla using a combined linguistic and psychological approach, despite the rise of fake news in Bangla-speaking regions. Our research aims to bridge this gap by developing tailored tools that effectively address the nuances of fake news in the Bangla language. By implementing a robust detection model, we contribute to a deeper understanding of misinformation in this context. In this work, we present a psycholinguistic method that combines linguistic and psychological features of the Bangla language to detect fake news. We created a dataset of 5,428 Bangla news articles, categorized as real or fake, capturing diverse topics and styles. This research highlights the importance of developing language-specific tools to combat misinformation. It also offers a valuable resource for future studies, helping to promote a more informed and responsible media environment in Bangla-speaking communities. Our hybrid model, which integrates Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP), effectively captures these features, achieving an accuracy of 96.32%. These results demonstrate the strong potential of our approach in improving the detection of fake news in Bangla.
Tanjim et al. (Sat,) studied this question.