Survey on Social Network Mental Disorder Detection via Online Social Media Mining using Machine Learning Framework

Abstract

The development in social network communication prompts the dangerous utilization. An increasing number of social networks mental disorders (SNMD), such as the dependence on the cybernetic relationship, the overload of information and the constriction of the networkhave beennoticed recently. Currently, the symptoms of these mental disorders are passively observed which causes late clinical intervention. In this paper, argue that the mining of online social behavior offers the opportunity to actively identify the SNMD at an early stage. It is difficult to detect SNMD because the mental state cannot be observed directly from the records of online social activities.This approach, new and innovative for the practice of SNMD detection, it is not based on the self-disclosure of these mental factors through questionnaires psychology. Instead, to propose a framework of machine learning, or the detection of mental disorders in social networks (SNMD), which exploits the features extracted from social network data to accurately identify potential SNMD cases. Alsouse multiple sources learning in SNMD and proposing a new SNMD-based tensor model (STM) to improve accuracy. To increase the scalability of STM, also further improve efficiency with performance guarantees. Thisframework is evaluated through a user study with no of users of the network. Also perform a feature analysis and also apply SNMD in large-scale data sets and analyze the characteristics of the three types of mental disorder.