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In recent years, due to the mental burden of depression, the number of peoplewho endanger their lives has been increasing rapidly. The online social network(OSN) provides researchers with another perspective for detecting individualssuffering from depression. However, existing studies of depression detectionbased on machine learning still leave relatively low classificationperformance, suggesting that there is significant improvement potential forimprovement in their feature engineering. In this paper, we manually build alarge dataset on Sina Weibo (a leading OSN with the largest number of activeusers in the Chinese community), namely Weibo User Depression Detection Dataset(WU3D). It includes more than 20,000 normal users and more than 10,000depressed users, both of which are manually labeled and rechecked byprofessionals. By analyzing the user's text, social behavior, and postedpictures, ten statistical features are concluded and proposed. In the meantime,text-based word features are extracted using the popular pretrained modelXLNet. Moreover, a novel deep neural network classification model, i.e.FusionNet (FN), is proposed and simultaneously trained with the above-extractedfeatures, which are seen as multiple classification tasks. The experimentalresults show that FusionNet achieves the highest F1-Score of 0.9772 on the testdataset. Compared to existing studies, our proposed method has betterclassification performance and robustness for unbalanced training samples. Ourwork also provides a new way to detect depression on other OSN platforms.

A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo