Study on Social Network Mental Disorder Detection Based Markov Model

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. 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, URL: https://www.ijtsrd.com/papers/ijtsrd21398.pdf