Populations, exposures and outcomes: How social media data is poised to advance climate-demography research
4 affiliations
- 1Division of Pharmaceutical Care and Health Systems, College of Pharmacy, University of Minnesota, Minneapolis, MN 55114
- 2Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55114
- 3Department of Geography, Environment and Society, Minnesota Population Center, University of Minnesota, Minneapolis, MN 55114
- 4Division of Health Informatics and School of Medicine, University of Minnesota, Minneapolis, MN 55114
Abstract
As the natural environment continues to change, there is a shared sense among researchers, policymakers, and health practitioners that we need to better understand the impacts of those changes on health and health behaviors for individuals and populations. “Big” social media data, including large datasets from platforms like X (formerly Twitter), Reddit, Facebook, TikTok, and YouTube, provide access to previously hard-to-reach populations. These data are posed to revolutionize the data landscape in climate change research; however, there are limitations and ethical concerns with data from these platforms, such as ethical questions, biases, noise, and a distinct underrepresentation of marginalized groups. These issues could hamper the adoption of social media data in research on climate change and health. In this commentary, we propose leveraging and augmenting a “human-in-the-loop” approach that uses humans in data extraction, quality assurances, and model testing to ensure data quality and overcome known limitations of social media data. Using examples from health data, we propose new horizons in climate change health research made possible with qualitative research methods.
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BibTeX
@article{mikal2026populations,
title = {{Populations, exposures and outcomes: How social media data is poised to advance climate-demography research}},
author = {Jude Mikal and Stevie Chancellor and Kathryn Grace and Sripirya Rajamani},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = oct,
doi = {10.1073/pnas.2321161122},
url = {https://doi.org/10.1073/pnas.2321161122},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Populations, exposures and outcomes: How social media data is poised to advance climate-demography research
AU - Jude Mikal
AU - Stevie Chancellor
AU - Kathryn Grace
AU - Sripirya Rajamani
PY - 2026
DA - 2026/10/26
JO - Proceedings of the National Academy of Sciences of the United States of America
DO - 10.1073/pnas.2321161122
UR - https://doi.org/10.1073/pnas.2321161122
AB - As the natural environment continues to change, there is a shared sense among researchers, policymakers, and health practitioners that we need to better understand the impacts of those changes on health and health behaviors for individuals and populations. “Big” social media data, including large datasets from platforms like X (formerly Twitter), Reddit, Facebook, TikTok, and YouTube, provide access to previously hard-to-reach populations. These data are posed to revolutionize the data landscape in climate change research; however, there are limitations and ethical concerns with data from these platforms, such as ethical questions, biases, noise, and a distinct underrepresentation of marginalized groups. These issues could hamper the adoption of social media data in research on climate change and health. In this commentary, we propose leveraging and augmenting a “human-in-the-loop” approach that uses humans in data extraction, quality assurances, and model testing to ensure data quality and overcome known limitations of social media data. Using examples from health data, we propose new horizons in climate change health research made possible with qualitative research methods.
SN - 0027-8424
SN - 1091-6490
ER -