  Compared to physical health, population mental health measurement in the U.S.
is very coarse-grained. Currently, in the largest population surveys, such as
those carried out by the Centers for Disease Control or Gallup, mental health
is only broadly captured through "mentally unhealthy days" or "sadness", and
limited to relatively infrequent state or metropolitan estimates. Through the
large scale analysis of social media data, robust estimation of population
mental health is feasible at much higher resolutions, up to weekly estimates
for counties. In the present work, we validate a pipeline that uses a sample of
1.2 billion Tweets from 2 million geo-located users to estimate mental health
changes for the two leading mental health conditions, depression and anxiety.
We find moderate to large associations between the language-based mental health
assessments and survey scores from Gallup for multiple levels of granularity,
down to the county-week (fixed effects $\beta = .25$ to $1.58$; $p<.001$).
Language-based assessment allows for the cost-effective and scalable monitoring
of population mental health at weekly time scales. Such spatially fine-grained
time series are well suited to monitor effects of societal events and policies
as well as enable quasi-experimental study designs in population health and
other disciplines. Beyond mental health in the U.S., this method generalizes to
a broad set of psychological outcomes and allows for community measurement in
under-resourced settings where no traditional survey measures - but social
media data - are available.
