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        <title>Enhancing Air Quality Prediction with Social Media and Natural Language Processing</title>
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    <name type="personal">
        <namePart type="given">Jyun-Yu</namePart>
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        <namePart type="given">Xue</namePart>
        <namePart type="family">Sun</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Sean</namePart>
        <namePart type="family">Young</namePart>
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        <dateIssued>2019-07</dateIssued>
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    <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.</abstract>
    <identifier type="citekey">jiang-etal-2019-enhancing</identifier>
    <identifier type="doi">10.18653/v1/P19-1251</identifier>
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        <url>https://aclanthology.org/P19-1251</url>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T Enhancing Air Quality Prediction with Social Media and Natural Language Processing
%A Jiang, Jyun-Yu
%A Sun, Xue
%A Wang, Wei
%A Young, Sean
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F jiang-etal-2019-enhancing
%X 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.
%R 10.18653/v1/P19-1251
%U https://aclanthology.org/P19-1251
%U https://doi.org/10.18653/v1/P19-1251
%P 2627-2632

Markdown (Informal)

[Enhancing Air Quality Prediction with Social Media and Natural Language Processing](https://aclanthology.org/P19-1251) (Jiang et al., ACL 2019)

• Enhancing Air Quality Prediction with Social Media and Natural Language Processing (Jiang et al., ACL 2019)

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.