Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates
作者机构：Department of ManagementInformation and Production EngineeringUniversity of BergamoBergamo24129Italy Department of Environmental SciencesInformatics and StatisticsCa’Foscari University of VeniceVenice30123Italy School of Computing and EngineeringUniversity of West LondonLondonW55RFUK Department of Engineering and Applied SciencesUniversity of BergamoBergamo24129Italy Durma College of Science and HumanitiesShaqra UniversityShaqra11961Saudi Arabia School of EngineeringUniversity of GlasgowGlasgowG128QQUK
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第72卷第8期
页 面：2411-2427页
核心收录：
学科分类：1002[医学-临床医学] 1010[医学-医学技术（可授医学、理学学位）] 100215[医学-康复医学与理疗学] 10[医学]
主 题：Psychotic behaviors mental health socialmedia machine learning
摘 要：With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades,social media platforms(such as Facebook,Twitter,and Instagram)have consumed a large proportion of time in our daily *** tend to stay alive on their social media with recent updates,as it has become the primary source of interactionwithin social *** social media platforms offer several remarkable features but are simultaneously prone to various critical *** studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression,anxiety,suicide commitment,and mental disorder,particularly in the young adults who have excessively spent time on socialmedia which necessitates a thorough psychological analysis of all these *** study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status *** this paper,we start with depression detection in the first instance and then expand on analyzing six other psychotic issues(e.g.,depression,anxiety,psychopathic deviate,hypochondria,unrealistic,and hypomania)commonly found in adults due to extreme use of social media *** classify the psychotic issues with the user’s mental state,we have employed different Machine Learning(ML)classifiers i.e.,Random Forest(RF),Support Vector Machine(SVM),Naïve Bayes(NB),and K-Nearest Neighbor(KNN).The used ML models are trained and tested by using different combinations of features selection *** observe themost suitable classifiers for psychotic issue classification,a cost-benefit function(sometimes termed as‘Suitability’)has been used which combines the accuracy of the model with its execution *** experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.
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