Citation

Yang T, Li F, Ji D, Liang X, Xie T, Tian S, Li B. Inf. Process. Manag. 2021; 58(6).

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.ipm.2021.102681

PMID

unavailable

Abstract

Depression is a widespread and intractable problem in modern society, which may lead to suicide ideation and behavior. Analyzing depression or suicide based on the posts of social media such as Twitter or Reddit has achieved great progress in recent years. However, most work focuses on English social media and depression prediction is typically formalized as being present or absent. In this paper, we construct a human-annotated dataset for depression analysis via Chinese microblog reviews which includes 6,100 manually-annotated posts. Our dataset includes two fine-grained tasks, namely depression degree prediction and depression cause prediction. The object of the former task is to classify a Microblog post into one of 5 categories based on the depression degree, while the object of the latter one is selecting one or multiple reasons that cause the depression from 7 predefined categories. To set up a benchmark, we design a neural model for joint depression degree and cause prediction, and compare it with several widely-used neural models such as TextCNN, BiLSTM and BERT. Our model outperforms the baselines and achieves at most 65+% F1 for depression degree prediction, 70+% F1 and 90+% AUC for depression cause prediction, which shows that neural models achieve promising results, but there is still room for improvement. Our work can extend the area of social-media-based depression analyses, and our annotated data and code can also facilitate related research. © 2021 Elsevier Ltd

Language: en

Keywords

Forecasting; Blogs; Natural language processing systems; Social networking (online); Social media; Natural language processing; Fine grained; Natural languages; Language processing; Annotated datasets; BERT; Depression analyse; Depression analysis; Microblogs; Multi-task learning; Multitask learning; Neural models