11月3日论文推荐（附下载地址） / 开普饭

论文名：

Cross-Domain Depression Detection via Harvesting Social Media

来源：IJCAI/2018

作者：Tiancheng Shen,Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He,

Huanbo Luan, Jie Tang, Thanassis Tiropanis, Tat-Seng Chua and Wendy Hall

推荐理由：

该论文通过社交网络例如Twitter、微博等社交网络平台的数据探测抑郁症人群。该论文首先系统地分析了跨领域的抑郁症相关特征模式，并总结了两个主要的检测挑战，即异构性和发散性。同时提出了一个带有特征自适应变换和组合策略的交叉领域的深度神经网络模型——DNN-FATC，来实现跨异构域传递相关信息。实验显示，与现有的异构迁移方法或直接在目标域进行训练相比该模型方法F1值提高了3.4%以上。

Abstract

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 depressionrelated feature patterns across domains and summarize two major detection challenges, namely isomerism and divergency. We further propose a crossdomain 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% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings.