Ali, M., Baquir, A., Sherazi, Hafiz Husnain Raza ORCID: https://orcid.org/0000-0001-8152-4065 and Imran, M.A. (2022) Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates. Computers, Materials and Continua, 72 (2). pp. 2411-2427.

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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, andInstagram)haveconsumedalargeproportion of time in our daily lives. People tend to stay alive on their social media with recent updates,asithasbecometheprimarysourceofinteractionwithinsocial 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 whohaveexcessively spent time on social media whichnecessitates 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, andhypomania)commonlyfoundinadultsduetoextremeuseofsocialmedia 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).TheusedMLmodelsaretrainedandtestedbyusingdifferentcombinationsoffeaturesselectiontechniques.Toobservethemostsuitable 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.

Item Type:	Article
Identifier:	10.32604/cmc.2022.024704
Subjects:	Computing
Related URLs:	Publisher
Depositing User:	Marc Forster
Date Deposited:	11 Nov 2024 13:41
Last Modified:	11 Nov 2024 13:45
URI:	https://repository.uwl.ac.uk/id/eprint/12877

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