Citation

Ali M, Baqir A, Sherazi HHR, Hussain A, Alshehri AH, Imran MA. Computers, Materials and Continua 2022; 72(2): 2411-2427.

Copyright

(Copyright © 2022)

DOI

10.32604/cmc.2022.024704

PMID

unavailable

Abstract

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 lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interactionwithin social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent 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 platforms. This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates. In 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 networks. To 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 usedMLmodels are trained and tested by using different combinations of features selection techniques. To 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 time. The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set. © 2022 Tech Science Press. All rights reserved.

Language: en

Keywords

Cost benefit analysis; mental health; Mental health; Social networking (online); Facebook; Decision trees; Support vector machines; Social media; Random forests; machine learning; Social media platforms; Machine-learning; Status updates; Nearest neighbor search; Behavior prediction; Psychotic behavior; Psychotic behaviors; socialmedia; Socialmedium