ArticleProceedings of the ACM on human-computer interaction2018
Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes.
Article in Proceedings of the ACM on human-computer interaction, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Structural racism and homophobia evaluated through social media sentiment combined with activity spaces and associations with mental health among young sexual minority men.Social science & medicine (1982) · 2023Article
- Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health.American journal of public health · 2022Review
- Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information.Annals of the American Association of Geographers · 2022Article
- Role of the Built and Online Social Environments on Expression of Dining on Instagram.International journal of environmental research and public health · 2020Article
- Quantifying depression-related language on social media during the COVID-19 pandemic.International journal of population data science · 2020Article
- Using Digital Data to Protect and Promote the Most Vulnerable in the Fight Against COVID-19.Frontiers in public health · 2020Article
- Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention.Current treatment options in psychiatry · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.