BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla
Mahammed Kamruzzaman Md. Minul Islam Shovon Gene Louis Kim
Initial work has been done to address fake news detection and
misrepresentation of news in the Bengali language. However, no work in Bengali
yet addresses the identification of specific claims in social media news that
falsely manipulates a related news article. At this point, this problem has
been tackled in English and a few other languages, but not in the Bengali
language. In this paper, we curate a dataset of social media content labeled
with information manipulation relative to reference articles, called BanMANI.
The dataset collection method we describe works around the limitations of the
available NLP tools in Bangla. We expect these techniques will carry over to
building similar datasets in other low-resource languages. BanMANI forms the
basis both for evaluating the capabilities of existing NLP systems and for
training or fine-tuning new models specifically on this task. In our analysis,
we find that this task challenges current LLMs both under zero-shot and
fine-tuned settings.