  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.
