{"type":"ScholarlyArticle","title":"Cross-Domain Depression Detection via Harvesting Social Media","authors":[{"name":"Tiancheng Shen","slug":"tiancheng-shen","given":"Tiancheng","family":"Shen","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China Key Laboratory of Pervasive Computing ,"},{"name":"Jia Jia","slug":"jia-jia","given":"Jia","family":"Jia","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China Key Laboratory of Pervasive Computing ,"},{"name":"Guangyao Shen","slug":"guangyao-shen","given":"Guangyao","family":"Shen","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China Key Laboratory of Pervasive Computing ,"},{"name":"Fuli Feng","slug":"fuli-feng","given":"Fuli","family":"Feng","affiliation":"School of Computing , National University of Singapore"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"School of Computing , National University of Singapore"},{"name":"Huanbo Luan","slug":"huanbo-luan","given":"Huanbo","family":"Luan","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China Key Laboratory of Pervasive Computing ,"},{"name":"Jie Tang","slug":"jie-tang","given":"Jie","family":"Tang","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China Key Laboratory of Pervasive Computing ,"},{"name":"Thanassis Tiropanis","slug":"thanassis-tiropanis","given":"Thanassis","family":"Tiropanis","affiliation":"Electronics and Computer Science , University of Southampton"},{"name":"Tat-Seng Chua","slug":"tat-seng-chua","given":"Tat-Seng","family":"Chua","affiliation":"School of Computing , National University of Singapore; Ministry of Education Beijing National Research Center for Information Science and Technology"},{"name":"Wendy Hall","slug":"wendy-hall","given":"Wendy","family":"Hall","affiliation":"Electronics and Computer Science , University of Southampton"}],"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 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% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings.","doi":"10.24963/ijcai.2018/223","url":"https://ijcai.org/proceedings/2018/223","pdf":"https://ijcai.org/proceedings/2018/0223.pdf","markdown":"https://ijcai.org/proceedings/2018/223.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/223","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Humans and AI","track":"Main track","number":223,"pages":{"first":1611,"last":1617},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Humans and AI: Personalization and User Modeling","Machine Learning Applications: Humanities"],"videos":[],"references":[{"position":1,"raw":"[Chattopadhyay et al., 2012] Rita Chattopadhyay, Qian Sun, Wei Fan, Ian Davidson, Sethuraman Panchanathan, and Jieping Ye. 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