# ESG-FTSE: A corpus of news articles with ESG relevance labels and use cases

arXiv: https://arxiv.org/abs/2405.20218 · HTML: https://mlsanity.com/paper/2405.20218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 8 of 8)

- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices](https://mlsanity.com/paper/2203.16788/) (2022)
- [Predicting the Effects of News Sentiments on the Stock Market](https://mlsanity.com/paper/1812.04199/) (2018)
- [CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction](https://mlsanity.com/paper/2204.03871/) (2022)
- [Prediction of ESG Compliance using a Heterogeneous Information Network](https://mlsanity.com/paper/1811.12166/) (2018)
- [ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction](https://mlsanity.com/paper/2005.02527/) (2020)
- [Empirical Study on Detecting Controversy in Social Media](https://mlsanity.com/paper/1909.01093/) (2019)

## Built on by (most cited first, 1 of 1)

- [Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling of ESG Report](https://mlsanity.com/paper/2511.16417/) (2025)

## Abstract (arXiv)

We present ESG-FTSE, the first corpus comprised of news articles with Environmental, Social and Governance (ESG) relevance annotations. In recent years, investors and regulators have pushed ESG investing to the mainstream due to the urgency of climate change. This has led to the rise of ESG scores to evaluate an investment's credentials as socially responsible. While demand for ESG scores is high, their quality varies wildly. Quantitative techniques can be applied to improve ESG scores, thus, responsible investing. To contribute to resource building for ESG and financial text mining, we pioneer the ESG-FTSE corpus. We further present the first of its kind ESG annotation schema. It has three levels: a binary classification (relevant versus irrelevant news articles), ESG classification (ESG-related news articles), and target company. Both supervised and unsupervised learning experiments for ESG relevance detection were conducted to demonstrate that the corpus can be used in different settings to derive accurate ESG predictions. Keywords: corpus annotation, ESG labels, annotation schema, news article, natural language processing