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Study on Social Network Mental Disorder Detection Based Markov Model

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Study on Social Network Mental Disorder Detection Based Markov Model


Teena Patidar | Prof. Avinash Sharma

https://doi.org/10.31142/ijtsrd21398



Teena Patidar | Prof. Avinash Sharma "Study on Social Network Mental Disorder Detection Based Markov Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2, February 2019, pp.415-418, URL: https://www.ijtsrd.com/papers/ijtsrd21398.pdf

With the explosive growth in popularity of social networking and messaging apps, online social networks (OSNs) have become a part of many people’s daily lives. There are many mental disorder encountered noticed of social network mental disorders (SNMDs), the basic parameter at which evaluate the mental level of user such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed day by day, resulting in delayed clinical intervention. In this work, the mining online social behaviour 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, now propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data log file to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMDD based Markov Model (SMM) to improve the accuracy. To increase the scalability of SMM, we further improve the efficiency with performance guarantee. Our framework is evaluated via 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.

online social networks, Cyber-Relationship Addiction, Information Overload and Net Compulsion, SNMDs, SMM


IJTSRD21398
Volume-3 | Issue-2, February 2019
415-418
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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