title: Social media as a sensor of air quality and public response in China.
abstract: BACKGROUND
  Recent studies have demonstrated the utility of social media data sources for a wide range of public health goals, including disease surveillance, mental health trends, and health perceptions and sentiment. Most such research has focused on English-language social media for the task of disease surveillance.
  
  
  OBJECTIVE
  We investigated the value of Chinese social media for monitoring air quality trends and related public perceptions and response. The goal was to determine if this data is suitable for learning actionable information about pollution levels and public response.
  
  
  METHODS
  We mined a collection of 93 million messages from Sina Weibo, China's largest microblogging service. We experimented with different filters to identify messages relevant to air quality, based on keyword matching and topic modeling. We evaluated the reliability of the data filters by comparing message volume per city to air particle pollution rates obtained from the Chinese government for 74 cities. Additionally, we performed a qualitative study of the content of pollution-related messages by coding a sample of 170 messages for relevance to air quality, and whether the message included details such as a reactive behavior or a health concern.
  
  
  RESULTS
  The volume of pollution-related messages is highly correlated with particle pollution levels, with Pearson correlation values up to .718 (n=74, P<.001). Our qualitative results found that 67.1% (114/170) of messages were relevant to air quality and of those, 78.9% (90/114) were a firsthand report. Of firsthand reports, 28% (32/90) indicated a reactive behavior and 19% (17/90) expressed a health concern. Additionally, 3 messages of 170 requested that action be taken to improve quality.
  
  
  CONCLUSIONS
  We have found quantitatively that message volume in Sina Weibo is indicative of true particle pollution levels, and we have found qualitatively that messages contain rich details including perceptions, behaviors, and self-reported health effects. Social media data can augment existing air pollution surveillance data, especially perception and health-related data that traditionally requires expensive surveys or interviews.
authors.affiliation: Johns Hopkins University, Department of Computer Science, Baltimore, MD, United States.
authors.forename: Shiliang
authors.identifier: http://orcid.org/0000-0003-1760-8664
authors.initials: S
authors.lastname: Wang
authors.forename: Michael J
authors.identifier: http://orcid.org/0000-0002-9149-7539
authors.initials: MJ
authors.lastname: Paul
authors.forename: Mark
authors.identifier: http://orcid.org/0000-0002-0422-2474
authors.initials: M
authors.lastname: Dredze
mesh_terms: D000397:Air Pollution; D002681:China; D057225:Data Mining; D004784:Environmental Monitoring / Q000379:methods*; D009323:Natural Language Processing; D062486:Public Health Surveillance; D015203:Reproducibility of Results; D061108:Social Media
keywords: air pollution; data mining; natural language processing; public health surveillance; social media; text mining
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