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Issue No. 07 - July (2018 vol. 30)
ISSN: 1041-4347
pp: 1212-1225
Hong-Han Shuai , Department of Electrical Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan
Chih-Ya Shen , Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
De-Nian Yang , Research Center of Information Technology Innovation, Academia Sinica, Taipei, Taiwan
Yi-Feng Carol Lan , Graduate Institute of Educational Psychology and Counseling, Tamkang University, New Taipei City, Taiwan
Wang-Chien Lee , Department of Computer Science and Engineering, Pennsylvania State University, PA
Philip S. Yu , Department of Computer Science, University of Illinois at Chicago, Chicago, IL
Ming-Syan Chen , Research Center of Information Technology Innovation, Academia Sinica, No. 128, Sec. 2, Academia Road, Taipei, Taiwan
The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.
Social network services, Feature extraction, Mental disorders, Psychology, Data mining, Tensile stress, Internet

H. Shuai et al., "A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 7, pp. 1212-1225, 2018.
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