Qualitative Analysis of Depression Models by Demographics
Models for identifying depression using social media text exhibit biases towards different gender and racial/ethnic groups. Factors like representation and balance of groups within the dataset are contributory factors, but difference in content and social media use may further explain these biases. We present an analysis of the content of social media posts from different demographic groups. Our analysis shows that there are content differences between depression and control subgroups across demographic groups, and that temporal topics and demographic-specific topics are correlated with downstream depression model error. We discuss the implications of our work on creating future datasets, as well as designing and training models for mental health.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models
Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention. For these models to be maximally useful,…
Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data
Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we need an understanding of how different demo…
Synthetic Data GenerationUnderneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction
Recent studies show bias in many machine learning models for depression detection, but bias in LLMs for this task remains unexplored. This work presents the first attempt to investigate the degree of gender bias present …
Depression DetectionFairnessLarge-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants
Digital phenotyping offers a novel and cost-efficient approach for managing depression and anxiety. Previous studies, often limited to small-to-medium or specific populations, may lack generalizability. We conducted a cr…
Clustering