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Initial work has been done to address fake news detection andmisrepresentation of news in the Bengali language. However, no work in Bengaliyet addresses the identification of specific claims in social media news thatfalsely manipulates a related news article. At this point, this problem hasbeen tackled in English and a few other languages, but not in the Bengalilanguage. In this paper, we curate a dataset of social media content labeledwith information manipulation relative to reference articles, called BanMANI.The dataset collection method we describe works around the limitations of theavailable NLP tools in Bangla. We expect these techniques will carry over tobuilding similar datasets in other low-resource languages. BanMANI forms thebasis both for evaluating the capabilities of existing NLP systems and fortraining or fine-tuning new models specifically on this task. In our analysis,we find that this task challenges current LLMs both under zero-shot andfine-tuned settings.

BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla