Mental disorders such as depression and anxiety have been increasing at
alarming rates in the worldwide population. Notably, the major depressive
disorder has become a common problem among higher education students,
aggravated, and maybe even occasioned, by the academic pressures they must
face. While the reasons for this alarming situation remain unclear (although
widely investigated), the student already facing this problem must receive
treatment. To that, it is first necessary to screen the symptoms. The
traditional way for that is relying on clinical consultations or answering
questionnaires. However, nowadays, the data shared at social media is a
ubiquitous source that can be used to detect the depression symptoms even when
the student is not able to afford or search for professional care. Previous
works have already relied on social media data to detect depression on the
general population, usually focusing on either posted images or texts or
relying on metadata. In this work, we focus on detecting the severity of the
depression symptoms in higher education students, by comparing deep learning to
feature engineering models induced from both the pictures and their captions
posted on Instagram. The experimental results show that students presenting a
BDI score higher or equal than 20 can be detected with 0.92 of recall and 0.69
of precision in the best case, reached by a fusion model. Our findings show the
potential of large-scale depression screening, which could shed light upon
students at-risk.

通过研究学生在社交媒体上发布的图片和标题，使用深度学习进行功能工程模型从而检测大学生抑郁症状严重程度的研究表明，具有 BDI 得分大于或等于 20 的学生可以在最佳情况下通过融合模型达到 0.92 的召回率和 0.69 的精度，这表明了大规模抑郁症筛查的潜力，从而有助于发现处于风险的学生。