Dhoroni: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processing

TL;DR

This paper introduces Dhoroni, a comprehensive Bengali climate change news dataset and a baseline NLP model, to improve climate discourse analysis and research in Bengali, addressing resource gaps in climate communication for Bangladesh.

Contribution

It presents the first large-scale Bengali climate news dataset and a novel NLP model family for opinion detection, filling critical research gaps.

Findings

Dataset contains 2300 annotated articles.

BanglaBERT-Dhoroni models effectively detect climate opinions.

Enhanced accessibility for Bengali climate discourse analysis.

Abstract

Climate change poses critical challenges globally, disproportionately affecting low-income countries that often lack resources and linguistic representation on the international stage. Despite Bangladesh's status as one of the most vulnerable nations to climate impacts, research gaps persist in Bengali-language studies related to climate change and NLP. To address this disparity, we introduce Dhoroni, a novel Bengali (Bangla) climate change and environmental news dataset, comprising a 2300 annotated Bangla news articles, offering multiple perspectives such as political influence, scientific/statistical data, authenticity, stance detection, and stakeholder involvement. Furthermore, we present an in-depth exploratory analysis of Dhoroni and introduce BanglaBERT-Dhoroni family, a novel baseline model family for climate and environmental opinion detection in Bangla, fine-tuned on our dataset. This research contributes significantly to enhancing accessibility and analysis of climate discourse in Bengali (Bangla), addressing crucial communication and research gaps in climate-impacted regions like Bangladesh with 180 million people.