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Using social media for air pollution detection-the case of Eastern China Smog

Shi, Yu LU and Gao, Han (2017) INFM10 20171
Department of Informatics
Abstract
Air pollution has become an urgent issue that affecting public health and people’s daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as ‘sensors’ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence,... (More)
Air pollution has become an urgent issue that affecting public health and people’s daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as ‘sensors’ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence, our research (1) determine the correlation between the volume of air quality-related messages and observed Air quality index (AQI) with the help of time series analysis model; (2) investigate further the impact of a salient change of air quality on the relationship between the people’s subjective perceptions regarding to air pollution released on the Weibo and the extent of air pollution through a co-word network analysis model. Our study illustrates that the discussions on social media about air quality reflect the level of air pollution when the air quality changes saliently. (Less)
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author
Shi, Yu LU and Gao, Han
supervisor
organization
course
INFM10 20171
year
type
H1 - Master's Degree (One Year)
subject
keywords
air pollution surveillance, social media analytics, text analysis, time series analysis, co-word network analysis
report number
INF17-013
language
English
id
8915002
date added to LUP
2017-06-21 12:55:33
date last changed
2017-06-21 12:55:33
@misc{8915002,
  abstract     = {Air pollution has become an urgent issue that affecting public health and people’s daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as ‘sensors’ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence, our research (1) determine the correlation between the volume of air quality-related messages and observed Air quality index (AQI) with the help of time series analysis model; (2) investigate further the impact of a salient change of air quality on the relationship between the people’s subjective perceptions regarding to air pollution released on the Weibo and the extent of air pollution through a co-word network analysis model. Our study illustrates that the discussions on social media about air quality reflect the level of air pollution when the air quality changes saliently.},
  author       = {Shi, Yu and Gao, Han},
  keyword      = {air pollution surveillance,social media analytics,text analysis,time series analysis,co-word network analysis},
  language     = {eng},
  note         = {Student Paper},
  title        = {Using social media for air pollution detection-the case of Eastern China Smog},
  year         = {2017},
}