  Incongruity between news headlines and the body content is a common method of
deception used to attract readers. Profitable headlines pique readers' interest
and encourage them to visit a specific website. This is usually done by adding
an element of dishonesty, using enticements that do not precisely reflect the
content being delivered. As a result, automatic detection of incongruent news
between headline and body content using language analysis has gained the
research community's attention. However, various solutions are primarily being
developed for English to address this problem, leaving low-resource languages
out of the picture. Bangla is ranked 7th among the top 100 most widely spoken
languages, which motivates us to pay special attention to the Bangla language.
Furthermore, Bangla has a more complex syntactic structure and fewer natural
language processing resources, so it becomes challenging to perform NLP tasks
like incongruity detection and stance detection. To tackle this problem, for
the Bangla language, we offer a graph-based hierarchical dual encoder (BGHDE)
model that learns the content similarity and contradiction between Bangla news
headlines and content paragraphs effectively. The experimental results show
that the proposed Bangla graph-based neural network model achieves above 90%
accuracy on various Bangla news datasets.
