Correlating Twitter Language with Community-Level Health Outcomes
Table 1: Overview of data sources.
['[BOLD] Name', '[BOLD] # tweets', '[BOLD] Year', '[EMPTY]']
The assigned locations are in the United States. The geotags are categorized into US-counties which represent the set of communities A. The target variables y are health-related variables, for example normalized mortality or prevalence rates. We focus on cancer and AHD mortality as well as on diabetes prevalence. Hence, the quantile-based predictions give a categorization of the Ridge regression predictions on a US-county level. The ranked topics assess what language might relate to higher or lower rates of the corresponding disease. Not all counties are covered in the publicly available datasets, usually being limited to more populous counties. The collected Tweets are from 2014 and 2015. The target variables are the union-averaged values from 2014 and 2015: if the target variable is available for both years the two values are averaged. Conversely, if a county data point is only available for one, but not both years, we use this standalone value.