Abstract

Abstract:In today’s world, it is undeniable that social media plays an important role inimpacting our culture, our economy and our overall view of the world. Socialmedia is a new forum that brings people to exchange idea, connect with, relateto, and mobilize for a cause, seek advice, and offer guidance. Most research onsocial network mining focuses on discovering the knowledge behind the data forimproving peoples life. While OSNs seemingly expand their users capability inincreasing social contacts, they may actually decrease the face-to-faceinterpersonal interactions in the real world. Due to the epidemic scale ofthese phenomena, new terms such as Phubbing (Phone Snubbing) and Nomophobia (NoMobile Phone Phobia) have been created to describe those who cannot stop usingmobile social networking apps. Some social network mental disorders (SNMDs),Journal of Psychiatry have reported that excessive use, depression, socialwithdrawal, and a range of other negative repercussions. we propose a machinelearning framework, namely, Social Network Mental Disorder Detection (SNMDD),that exploits features extracted from social network data to accuratelyidentify potential cases of SNMDs. We also exploit multi-source learning inSNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy.We can find out the stressed users on social media platforms.