# Polarity Detection of Sustainable Development Goals in News Text

arXiv: https://arxiv.org/abs/2509.19833 · HTML: https://mlsanity.com/paper/2509.19833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 11 of 11)

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- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Which *BERT? A Survey Organizing Contextualized Encoders](https://mlsanity.com/paper/2010.00854/) (2020)
- [ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices](https://mlsanity.com/paper/2203.16788/) (2022)
- [BERT-Based Sentiment Analysis: A Software Engineering Perspective](https://mlsanity.com/paper/2106.02581/) (2021)
- [SWEAT: Scoring Polarization of Topics across Different Corpora](https://mlsanity.com/paper/2109.07231/) (2021)
- [Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis](https://mlsanity.com/paper/2403.08217/) (2024)

## Abstract (arXiv)

The United Nations' Sustainable Development Goals (SDGs) provide a globally recognised framework for addressing major societal, environmental, and economic challenges. While recent advances in natural language processing (NLP) and large language models (LLMs) have enabled the automatic identification of SDG-related content, they do not capture whether the described events represent progress toward or regression from a specific goal. To address this gap, we introduce the novel task of SDG polarity detection and present SDG-POD, a benchmark dataset combining manually annotated and synthetically generated examples. We evaluate six state-of-the-art open-source LLMs under both zero-shot and fine-tuning settings and investigate the impact of synthetic data augmentation on model performance. Our results show that SDG polarity detection remains challenging for current LLMs; however, fine-tuned models, particularly QWQ-32B, achieve the best overall performance, with especially strong results on SDG-9, SDG-12, and SDG-15. Furthermore, we demonstrate that synthetic training data consistently improves model robustness and classification performance. This work introduces a new benchmark for SDG polarity detection and provides practical insights into developing LLM-based systems for sustainability monitoring.