  Social media offers a unique window into attitudes like racism and
homophobia, exposure to which are important, hard to measure and understudied
social determinants of health. However, individual geo-located observations
from social media are noisy and geographically inconsistent. Existing areas by
which exposures are measured, like Zip codes, average over irrelevant
administratively-defined boundaries. Hence, in order to enable studies of
online social environmental measures like attitudes on social media and their
possible relationship to health outcomes, first there is a need for a method to
define the collective, underlying degree of social media attitudes by region.
To address this, we create the Socio-spatial-Self organizing map, "SS-SOM"
pipeline to best identify regions by their latent social attitude from Twitter
posts. SS-SOMs use neural embedding for text-classification, and augment
traditional SOMs to generate a controlled number of non-overlapping,
topologically-constrained and topically-similar clusters. We find that not only
are SS-SOMs robust to missing data, the exposure of a cohort of men who are
susceptible to multiple racism and homophobia-linked health outcomes, changes
by up to 42% using SS-SOM measures as compared to using Zip code-based
measures.
