arXiv:2211.07932
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics
원문 초록
Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms across gender and racial/ethnic groups. First, we use topic models and other NLP tools to find thematic and vocabulary differences when reporting stressors across demographic groups. We train language models using self-reported stressors to predict depressive symptoms, finding a relationship between stressors and depression. Finally, we find that differences in stressors translate to downstream performance differences across demographic groups.
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Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics
Evaluating Negative Sampling Approaches for Neural Topic Models
Seeded Poisson Factorization: Leveraging domain knowledge to fit topic models
Refining Dimensions for Improving Clustering-based Cross-lingual Topic Models
Locating the Leading Edge of Cultural Change
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