Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics

• URL: http://arxiv.org/abs/2211.07932v1
• Date: Tue, 15 Nov 2022 06:34:58 GMT
• Title: Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics
• Authors: Carlos Aguirre, Mark Dredze, Philip Resnik
• Abstract summary: We investigate the relationship between open-ended text responses about stressors and depressive symptoms across gender and racial/ethnic groups. 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.
• Score: 22.476706522778994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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