Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twitter (2024.naacl-short)
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| Challenge: | Social media data is used for detecting users with mental disorders, but public datasets lack crucial metadata related to this aspect. |
| Approach: | They use a custom geo-located Twitter dataset to evaluate the generalization of depressiondetection models on cross-cultural Twitter data. |
| Outcome: | The proposed models perform worse on Global South users compared to Global North. |
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| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
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| Challenge: | Existing approaches to identify mental health conditions using social media are limited by the presence of symptoms described in a questionnaire used by clinicians. |
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Amey Hengle, Atharva Kulkarni, Shantanu Patankar, Madhumitha Chandrasekaran, Sneha D’silva, Jemima Jacob, Rashmi Gupta
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| Challenge: | Social media are heavily used by many users to share their mental health concerns and diagnoses. |
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Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
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| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
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