Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset
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arXiv
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| Format: | Preprint |
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2024
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| author | Milintsevich, Kirill Sirts, Kairit Dias, Gaël |
| author_facet | Milintsevich, Kirill Sirts, Kairit Dias, Gaël |
| contents | This paper addresses the quality of annotations in mental health datasets used for NLP-based depression level estimation from social media texts. While previous research relies on social media-based datasets annotated with binary categories, i.e. depressed or non-depressed, recent datasets such as D2S and PRIMATE aim for nuanced annotations using PHQ-9 symptoms. However, most of these datasets rely on crowd workers without the domain knowledge for annotation. Focusing on the PRIMATE dataset, our study reveals concerns regarding annotation validity, particularly for the lack of interest or pleasure symptom. Through reannotation by a mental health professional, we introduce finer labels and textual spans as evidence, identifying a notable number of false positives. Our refined annotations, to be released under a Data Use Agreement, offer a higher-quality test set for anhedonia detection. This study underscores the necessity of addressing annotation quality issues in mental health datasets, advocating for improved methodologies to enhance NLP model reliability in mental health assessments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00438 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset Milintsevich, Kirill Sirts, Kairit Dias, Gaël Computation and Language This paper addresses the quality of annotations in mental health datasets used for NLP-based depression level estimation from social media texts. While previous research relies on social media-based datasets annotated with binary categories, i.e. depressed or non-depressed, recent datasets such as D2S and PRIMATE aim for nuanced annotations using PHQ-9 symptoms. However, most of these datasets rely on crowd workers without the domain knowledge for annotation. Focusing on the PRIMATE dataset, our study reveals concerns regarding annotation validity, particularly for the lack of interest or pleasure symptom. Through reannotation by a mental health professional, we introduce finer labels and textual spans as evidence, identifying a notable number of false positives. Our refined annotations, to be released under a Data Use Agreement, offer a higher-quality test set for anhedonia detection. This study underscores the necessity of addressing annotation quality issues in mental health datasets, advocating for improved methodologies to enhance NLP model reliability in mental health assessments. |
| title | Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.00438 |