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

arXiv: https://arxiv.org/abs/2410.17225 · HTML: https://mlsanity.com/paper/2410.17225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://mlsanity.com/paper/2012.00614/) (2020)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Detecting Stance in Media on Global Warming](https://mlsanity.com/paper/2010.15149/) (2020)
- [CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://mlsanity.com/paper/2307.15770/) (2023)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System](https://mlsanity.com/paper/2304.00477/) (2023)
- [Towards Climate Awareness in NLP Research](https://mlsanity.com/paper/2205.05071/) (2022)
- [Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques](https://mlsanity.com/paper/2401.06817/) (2024)

## Abstract (arXiv)

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.