Research Area: Machine Learning
Depression detection is a significant issue for human well-being. In previous studies, online detection has proven effective in Twitter, enabling proactive care for depressed users. Owing to cultural differences, replicating the method to other social media platforms, such as Chinese Weibo, however, might lead to poor performance because of insufficient available labeled (self-reported depression) data for model training. In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e.g. Weibo) with ample Twitter data as the source domain. We first systematically analyze the depression-related feature patterns across domains and summarize two major detection challenges, namely isomerism and divergency. We further propose a cross-domain Deep Neural Network model with Feature Adaptive Transformation & Combination strategy (DNN-FATC) that transfers the relevant information across heterogeneous domains. Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3.4 percent improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings.
Keywords:
Cross-Domain
Depression Detection
Harvesting Social Media
Machine Learning
Deep Learning
Author(s) Name: Tiancheng Shen, Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He, Huanbo Luan, Jie Tang, T. Tiropanis, Tat-Seng Chua, W. Hall
Journal name: Computer Science
Conferrence name:
Publisher name: Semantic Scholar
DOI: 10.24963/ijcai.2018/223
Volume Information:
