Socio-spatial self-organizing maps: Using social media to assess relevant geographies for exposure to social processes

Kunal Relia, Mohammad Akbari, Dustin Duncan, Rumi Chunara

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number145
JournalProceedings of the ACM on Human-Computer Interaction
Volume2
Issue numberCSCW
DOIs
StatePublished - Nov 1 2018

Fingerprint

Self organizing maps
social process
social media
Health
geography
racism
health
social attitude
twitter
Pipelines
determinants
homophobia

Keywords

  • Clustering
  • Homophobia
  • Racism
  • Self-organizing maps

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Social Sciences (miscellaneous)

Cite this

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title = "Socio-spatial self-organizing maps: Using social media to assess relevant geographies for exposure to social processes",
abstract = "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.",
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