Depression Mimicking Expressions

 The proposed dataset is designed to address the growing use of social media platforms for expressing mental health struggles, including severe depression. Existing datasets often suffer from an imbalance between depressive and non-depressive instances and fail to account for depression-mimicking expressions—such as stress, anxiety, sadness, sarcasm, and complaints—which are frequently misclassified as severe depression due to linguistic similarities. This leads to a high rate of false alarms and undermines the reliability of detection systems. To overcome these limitations, this dataset includes newly curated and annotated social media posts that not only identify instances of severe depression but also distinguish them from depression-mimicking expressions. The curated dataset makes up 60% of the total, while the remaining 40% is sourced from existing datasets focused on severe depression (refer to the links below). Together, this combination yields a total of 38,017 text samples. This dataset aims to provide better training for machine learning models and facilitate more realistic evaluations for accurately detecting severe depression.MHB: https://www.dropbox.com/scl/fo/y7p04go4bvvcwojalcwie/AAhiHEVm8jj_mVcGJB7JmPc?rlkey=gvl1q8zgru1drkloa0og27fgq&e=1&dl=0Dreaddit: https://github.com/gillian850413/Insight_Stress_Analysis/tree/master/data GoEmotions: https://github.com/google-research/google-research/tree/master/goemotions/data 

本研究提出的数据集旨在应对当前社交媒体平台愈发广泛用于表达心理健康困境（包括重度抑郁）的现状。现有数据集普遍存在抑郁与非抑郁样本分布失衡的问题，且未能覆盖抑郁模仿表达——诸如压力、焦虑、悲伤、讽刺与抱怨等——这类因语言特征相似而常被误判为重度抑郁的内容，此类问题会导致较高的误报率，进而削弱抑郁检测系统的可靠性。
为克服上述局限，本数据集收录了经全新整理与标注的社交媒体帖文，不仅可识别重度抑郁相关表述，还能将其与抑郁模仿表达明确区分。本次整理的新增数据集占总样本量的60%，剩余40%源自已有的重度抑郁专项数据集（详见下方链接）。二者结合后，总计包含38017条文本样本。
本数据集旨在为机器学习模型提供更优质的训练数据，并为精准检测重度抑郁的任务构建更贴合实际场景的评估基准。
MHB: https://www.dropbox.com/scl/fo/y7p04go4bvvcwojalcwie/AAhiHEVm8jj_mVcGJB7JmPc?rlkey=gvl1q8zgru1drkloa0og27fgq&e=1&dl=0
Dreaddit: https://github.com/gillian850413/Insight_Stress_Analysis/tree/master/data
GoEmotions: https://github.com/google-research/google-research/tree/master/goemotions/data