The Sexual and Gender-Diverse Face More Health Challenges During COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing

Abstract

Background: The COVID-19 pandemic has caused a disproportionate impact on the sex and gender diversity (SGD) community. Compared with non-SGD populations, their social relations and health status are more vulnerable, whereas public health data regarding SGD is scarce. Methods: : To analyze the concerns and health status of SGD individuals, this cohort study leveraged 471,371,477 tweets from 251,455 SGD and 22,644,411 non-SGD users, spanning from February 1, 2020, to April 30, 2022. The outcome measures comprised the distribution and dynamics of COVID-related topics, attitudes towards vaccines and the prevalence of symptoms. Results: : Topic analysis revealed that SGD users engaged more frequently in discussions related to “friends and family” (20.5% vs 13.1%, P Conclusion: The results provide insights to an understanding of the unique needs and experiences of the SGD community during the pandemic, emphasizing the value of social media data in epidemiological and public health research.

Contribution highlights

• Background: The COVID-19 pandemic has caused a disproportionate impact on the sex and gender diversity (SGD) community.
• Compared with non-SGD populations, their social relations and health status are more vulnerable, whereas public health data regarding SGD is scarce.

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

DOI

10.34133/hds.0127

PMID

39247070

PMCID

PMC11378377

Version relationship

• Preprint: https://doi.org/10.34133/hds.0127
• Version of record: https://doi.org/10.34133/hds.0127

BibTeX

@article{Zhang2024Sexualgender,
  title = {The Sexual and Gender-Diverse Face More Health Challenges During COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing},
  author = {Zhiyun Zhang and Yining Hua and Peilin Zhou and Shixu Lin and Minghui Li and Yujie Zhang and Li Zhou and Yanhui Liao and Jie Yang},
  journal = {Health Data Science},
  year = {2024},
  url = {https://spj.science.org/doi/10.34133/hds.0127},
  doi = {10.34133/hds.0127},
  pmid = {39247070}
}