[Submitted on 1 Apr 2024]
View a PDF of the paper titled Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twitter, by Nuredin Ali and 3 other authors
Abstract:Social media data has been used for detecting users with mental disorders, such as depression. Despite the global significance of cross-cultural representation and its potential impact on model performance, publicly available datasets often lack crucial metadata related to this aspect. In this work, we evaluate the generalization of benchmark datasets to build AI models on cross-cultural Twitter data. We gather a custom geo-located Twitter dataset of depressed users from seven countries as a test dataset. Our results show that depression detection models do not generalize globally. The models perform worse on Global South users compared to Global North. Pre-trained language models achieve the best generalization compared to Logistic Regression, though still show significant gaps in performance on depressed and non-Western users. We quantify our findings and provide several actionable suggestions to mitigate this issue.
Submission history
From: Nuredin Ali Abdelkadir [view email]
[v1]
Mon, 1 Apr 2024 03:59:12 UTC (8,734 KB)
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