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 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.

Keywords: Clustering; homophobia; racism; self-organizing maps.

Fig. 1.

The SS-SOM pipeline. (a) Tweets made within the NYC bounding box are classified as (b) racism vs. no racism and homophobia vs. no homophobia using a shallow neural network, where the resulting probabilities of Tweet is used to classify the Tweet. (c) NYC is divided into grid cells, geo-location of each Tweet is used to map Tweets to the cells, and the normalized count is represented by different colors of the grid cells - from red (high racism/homophobia) to green (no racism/homophobia). SS-SOM clustering is performed (example boundary represented by the dark blue border). (d) Resulting SS-SOM map for prevalence of racism in NYC.

Fig. 2.

(a) Map of NYC depicting prevalence of social media measured racism by Zip code; (b) homophobia by Zip code; (c) racism by SS-SOM with 94 clusters (threshold of 3); (d) homophobia by SS-SOM with 102 clusters (threshold of 3). It is clearly visible that areas of high/low exposure are resolved differently using SS-SOMs versus Zip codes. The white-areas in (c) and (d) within the city represent grid cells that had 0 Tweets, hence normalization for such grid cells was not possible.

Fig. 3.

(a) ROC curve for racism and homophobia classification using neural embedding and SVM. (b) Number of clusters and similarity between Zip codes and SS-SOM by threshold value. (c) Mean squared prediction error (MSPE) for SS-SOM (threshold of 3) across different proportions of held out grid cells (shaded region represents 95% confidence interval).

Fig. 4.

NYC map displaying areas of online homophobia/racism prevalence for geographic reference.