Paper Info
Title
Mining Online Social Data for Detecting Social Network Mental Disorders.
Abstract
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 factors considered in standard diagnostic criteria (questionnaire) cannot be 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. 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 SNMDbased Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 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 show that SNMDD is promising for identifying online social network users with potential SNMDs.
Year
DOI
Venue
2016
10.1145/2872427.2882996
WWW
Keywords
Field
DocType
Online social network, mental disorder detection, feature extraction, tensor factorization
Data mining,Information overload,Social network,Computer science,Social activity,Artificial intelligence,World Wide Web,Addiction,Exploit,Feature extraction,Tensor factorization,Machine learning,Pattern recognition (psychology)
Conference
Citations 
PageRank 
References 
9
0.49
22
Authors
7
Name
Order
Citations
PageRank
Hong-Han Shuai110024.80
Chih-Ya Shen210317.13
De-Nian Yang358666.66
Yi-Feng Lan4141.93
Wang-Chien Lee55765346.32
Philip S. Yu6306703474.16
Ming Chen765071277.71