Year of Award
Master of Science (MS)
Department or School/College
Douglas Brinkerhoff PhD
Douglas Brinkerhoff PhD, Erin Landguth PhD, Jesse Johnson PhD
twitter, pm2.5, sentiment, scraping, mining, autoregression
University of Montana
Fine particulate matter (PM2.5) is a known pollutant with clinically detrimental physiological and behavioral effects. We consider Twitter sentiment as a potential indicator for well-being in communities impacted by wildfire-associated PM2.5 across Montana and Idaho spanning 5 years (2014-2018). From these geospatial air quality data and geo-tagged tweets, we trained county level models to examine the power of Twitter sentiment as a function of PM2.5. For all 24 counties sampled, we found between 1 and 8 affective dimensions where a positive �� 2 was detected with a significant F-statistic (�� < 0.05). Specifically, we show that sentiment for anticipation in the wildfire-prone county of Missoula, MT yielded respective training/test set �� 2 of 0.0958 and 0.0686 with a p-value for the F-statistic of 3.09E-07. These analyses support social media sentiment as a potential public health metric by showing one of the first observations of a relationship between PM2.5 and Twitter sentiment.
Kelly, Matthew, "MODELING TWITTER SENTIMENT AS A FUNCTION OF PARTICULATE MATTER 2.5 FOR COMMUNITIES IMPACTED BY WILDFIRE ACROSS MONTANA AND IDAHO" (2020). Graduate Student Theses, Dissertations, & Professional Papers. 11689.
© Copyright 2020 Matthew Kelly