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Type: Journal Article
Author(s): Sonya Sachdeva; Sarah M. McCaffrey; Dexter Locke
Publication Date: 2017

Wildfires have significant effects on human populations, economically, environmentally, and in terms of their general well-being. Smoke pollution, in particular, from either prescribed burns or uncontrolled wildfires, can have significant health impacts. Some estimates suggest that smoke dispersion from fire events may affect the health of one in three residents in the United States, leading to an increased incidence of respiratory illnesses such as asthma and pulmonary disease. Scarcity in the measurements of particulate matter responsible for these public health issues makes addressing the problem of smoke dispersion challenging, especially when fires occur in remote regions. Crowdsourced data have become an essential component in addressing other societal problems (e.g., disaster relief, traffic congestion) but its utility in monitoring air quality impacts of wildfire events is unexplored. In this study, we assessed if user-generated social media content can be used as a complementary source of data in measuring particulate pollution from wildfire smoke. We found that the frequency of daily tweets within a 40,000 km2 area was a significant predictor of PM2.5 levels, beyond daily and geographic variation. These results suggest that social media can be a valuable tool for the measurement of air quality impacts of wildfire events, particularly in the absence of data from physical monitoring stations. Also, an analysis of the semantic content in people’s tweets provided insight into the socio-psychological dimensions of fire and smoke and their impact on people residing in, working in, or otherwise engaging with affected areas.

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Citation: Sachdeva, Sonya; McCaffrey, Sarah; Locke, Dexter. 2017. Social media approaches to modeling wildfire smoke dispersion: spatiotemporal and social scientific investigations. Information, Communication & Society 20(8):1146-1161.

Cataloging Information

  • 2014 King Fire
  • air quality
  • crowdsourcing
  • Generalized Additive Models
  • pollution
  • risk perception
  • smoke dispersion
  • social media
  • wildfires
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Record Maintained By: FRAMES Staff (
FRAMES Record Number: 24226