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Observing Social Web for Smog Disaster Forecasting

Published: 28 June 2015 Publication History

Abstract

Smog disasters are greatly affected by social activities such as driving. In this poster, we observe social web to enhance smog disaster forecasting. Different kinds of social indicators are measured from social web data with a social web data processing framework, and then evaluated for smog disaster forecasting with two experiments.

References

[1]
S. Mei, H. Li, J. Fan, X. Zhu, and C. R. Dyer. Inferring air pollution by sniffing social media. In ASONAM 2014, pages 534--539. IEEE, 2014.
[2]
Y. Zheng, F. Liu, and H.-P. Hsieh. U-air: When urban air quality inference meets big data. In KDD 2013, pages 1436--1444. ACM, August 2013.

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  • (2017)Forecasting smog-related health hazard based on social media and physical sensorInformation Systems10.1016/j.is.2016.03.01164:C(281-291)Online publication date: 1-Mar-2017

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  1. Observing Social Web for Smog Disaster Forecasting

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    cover image ACM Conferences
    WebSci '15: Proceedings of the ACM Web Science Conference
    June 2015
    366 pages
    ISBN:9781450336727
    DOI:10.1145/2786451
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 June 2015

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    Author Tags

    1. Forecasting
    2. Smog Disaster
    3. Social Web
    4. Weibo

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    WebSci '15
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    WebSci '15: ACM Web Science Conference
    June 28 - July 1, 2015
    Oxford, United Kingdom

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    Overall Acceptance Rate 245 of 933 submissions, 26%

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    Cited By

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    • (2017)Forecasting smog-related health hazard based on social media and physical sensorInformation Systems10.1016/j.is.2016.03.01164:C(281-291)Online publication date: 1-Mar-2017

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