# The Language of Weather: Social Media Reactions to Weather Accounting for Climatic and Linguistic Baselines

arXiv: https://arxiv.org/abs/2407.07683 · HTML: https://mlsanity.com/paper/2407.07683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination](https://mlsanity.com/paper/2406.08818/) (2024)
- [Investigating Gender Bias in BERT](https://mlsanity.com/paper/2009.05021/) (2020)
- [SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment](https://mlsanity.com/paper/1806.05521/) (2018)
- [Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://mlsanity.com/paper/1606.02820/) (2016)
- [Social Sensing of Floods in the UK](https://mlsanity.com/paper/1711.04695/) (2017)
- [The Human Geography of Twitter](https://mlsanity.com/paper/1807.04107/) (2018)
- [What a Nasty day: Exploring Mood-Weather Relationship from Twitter](https://mlsanity.com/paper/1410.8749/) (2014)
- [Weather impacts expressed sentiment](https://mlsanity.com/paper/1709.00071/) (2017)
- [Adverse weather amplifies social media activity](https://mlsanity.com/paper/2302.08456/) (2023)
- [Short text classification with machine learning in the social sciences: The case of climate change on Twitter](https://mlsanity.com/paper/2310.04452/) (2023)

## Abstract (arXiv)

This study explores how different weather conditions influence public sentiment on social media, focusing on Twitter data from the UK. By considering climate and linguistic baselines, we improve the accuracy of weather-related sentiment analysis. Our findings show that emotional responses to weather are complex, influenced by combinations of weather variables and regional language differences. The results highlight the importance of context-sensitive methods for better understanding public mood in response to weather, which can enhance impact-based forecasting and risk communication in the context of climate change.