Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes.

Authors:
Kunal Relia
Kunal Relia
Tandon School of Engineering
Dustin Duncan
Dustin Duncan
Harvard School of Public Health
United States
Rumi Chunara
Rumi Chunara
Harvard Medical School
United States

Proc ACM Hum Comput Interact 2018 Nov;2

New York University, USA.

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 nonoverlapping, 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.

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Source
http://dx.doi.org/10.1145/3274414DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448781PMC
November 2018

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