BanFakeNews: A Dataset for Detecting Fake News in Bangla
This addresses the problem of fake news propagation in Bangla, a low-resource language, for researchers and developers, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of resources for detecting fake news in low-resource languages by creating BanFakeNews, an annotated dataset of ~50K news articles in Bangla, and developed a benchmark system using NLP techniques to identify fake news.
Observing the damages that can be done by the rapid propagation of fake news in various sectors like politics and finance, automatic identification of fake news using linguistic analysis has drawn the attention of the research community. However, such methods are largely being developed for English where low resource languages remain out of the focus. But the risks spawned by fake and manipulative news are not confined by languages. In this work, we propose an annotated dataset of ~50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. Additionally, we provide an analysis of the dataset and develop a benchmark system with state of the art NLP techniques to identify Bangla fake news. To create this system, we explore traditional linguistic features and neural network based methods. We expect this dataset will be a valuable resource for building technologies to prevent the spreading of fake news and contribute in research with low resource languages.