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  4. A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
 
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A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

Journal
IEEE Transactions on Knowledge and Data Engineering
Journal Volume
30
Journal Issue
7
Pages
1212-1225
Date Issued
2018
Author(s)
Shuai
H.-H.
Shen
C.-Y.
Yang
D.-N.
Lan
Y.-F.C.
Lee
W.-C.
Yu
P.S.
MING-SYAN CHEN  
DOI
10.1109/TKDE.2017.2786695
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039809159&doi=10.1109%2fTKDE.2017.2786695&partnerID=40&md5=1157e7d01e5ff41eada0ff1a069a76ef
https://scholars.lib.ntu.edu.tw/handle/123456789/405573
Abstract
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. ? 1989-2012 IEEE.
Subjects
feature extraction; mental disorder detection; online social network; Tensor factorization acceleration
SDGs

[SDGs]SDG3

Other Subjects
Data mining; Extraction; Feature extraction; Internet; Learning systems; Surveys; Tensile stress; Mental disorders; On-line social networks; Psychology; Social network services; Tensor factorization; Social networking (online)
Type
journal article

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To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

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