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 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 manifest that SNMDD is
promising for identifying online social network users with potential SNMDs.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Hong-Han Shuai, Chih-Ya Shen, De-Nian Yang, Senior Member, IEEE, Yi-Feng Lan, Wang-Chien Lee, Philip S.
Yu, Fellow, IEEE and Ming-Syan Chen, Fellow, IEEE, “A Comprehensive
Study on Social Network Mental Disorders Detection via Online Social Media
Mining”, 2018