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.

评估跨文化 Twitter 数据对建立跨文化 AI 模型的泛化性，结果显示抑郁症检测模型在全球化方面表现较差，特别是在全球南方用户中，预训练语言模型在泛化性方面表现最佳，但在抑郁和非西方用户的表现仍存在差距。我们对发现进行量化，并提供了一些建议来缓解此问题。