Enhancing Air Quality Prediction with Social Media and Natural Language Processing

Jyun-Yu Jiang, Xue Sun, Wei Wang, Sean Young


Abstract
Accompanied by modern industrial developments, air pollution has already become a major concern for human health. Hence, air quality measures, such as the concentration of PM2.5, have attracted increasing attention. Even some studies apply historical measurements into air quality forecast, the changes of air quality conditions are still hard to monitor. In this paper, we propose to exploit social media and natural language processing techniques to enhance air quality prediction. Social media users are treated as social sensors with their findings and locations. After filtering noisy tweets using word selection and topic modeling, a deep learning model based on convolutional neural networks and over-tweet-pooling is proposed to enhance air quality prediction. We conduct experiments on 7-month real-world Twitter datasets in the five most heavily polluted states in the USA. The results show that our approach significantly improves air quality prediction over the baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores.
Anthology ID:
P19-1251
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2627–2632
Language:
URL:
https://aclanthology.org/P19-1251
DOI:
10.18653/v1/P19-1251
Bibkey:
Cite (ACL):
Jyun-Yu Jiang, Xue Sun, Wei Wang, and Sean Young. 2019. Enhancing Air Quality Prediction with Social Media and Natural Language Processing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2627–2632, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Enhancing Air Quality Prediction with Social Media and Natural Language Processing (Jiang et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1251.pdf