title: Inferring Atmospheric Particulate Matter Concentrations from Chinese Social Media Data.
abstract: Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011-2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported.
authors.affiliation: Department of Computer Science, Rice University, Houston, Texas, United States of America.
authors.forename: Zhu
authors.initials: Z
authors.lastname: Tao
authors.affiliation: Department of Media Studies, University of Virginia, Charlottesville, Virginia, United States of America.
authors.forename: Aynne
authors.initials: A
authors.lastname: Kokas
authors.affiliation: Department of Civil and Environmental Engineering, Rice University, Houston, Texas, United States of America.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Department of Civil and Environmental Engineering, Rice University, Houston, Texas, United States of America.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Cohan
authors.affiliation: Department of Computer Science, Rice University, Houston, Texas, United States of America.
authors.forename: Dan
authors.initials: D
authors.lastname: Wallach
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D000068476:Beijing; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D052638:Particulate Matter / Q000032:analysis*; D061108:Social Media
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter