Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory

Authors

  • Cory J. Cascalheira New Mexico State University Syracuse University
  • Shah Muhammad Hamdi New Mexico State University
  • Jillian R. Scheer Syracuse University
  • Koustuv Saha Microsoft Research
  • Soukaina Filali Boubrahimi Utah State University
  • Munmun De Choudhury Georgia Institute of Technology

Keywords:

Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health, Human computer interaction; social media tools; navigation and visualization, Qualitative and quantitative studies of social media

Abstract

Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay, transgender) people experience minority stress (i.e., identity-based stress arising from adverse social conditions). Given that minority stress is the leading framework for understanding health inequity among SGM people, researchers and clinicians need accurate methods to detect minority stress. Since social media fulfills important developmental, affiliative, and coping functions for SGM people, social media may be an ecologically valid channel for detecting minority stress. In this paper, we propose a bidirectional long short-term memory (BI-LSTM) network for classifying minority stress dis-closed on Reddit. Our experiments on a dataset of 12,645 Reddit posts resulted in an average accuracy of 65%.

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Published

2022-05-31

How to Cite

Cascalheira, C. J., Hamdi, S. M., Scheer, J. R., Saha, K., Boubrahimi, S. F., & Choudhury, M. D. (2022). Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 1373-1377. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19390