Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

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 interaction within 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 social media 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 used ML models are trained and tested by using different combinations of features selection techniques. To observe the most 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.

Internet-based identification of anxiety in university students using text and facial emotion analysis

“…As in the present exploration, the aforementioned studies focus on the use of machine learning for automated anxiety prediction. They explore different methods, such as analysing Facebook status updates to detect depression ( Ali et al, 2022 ) and predicting suicide risk and mental health problems on Twitter through multitask learning ( Benton et al, 2017 ) and depression prediction ( de Souza et al, 2022 ) on Reddit. Regarding facial expression recognition, some works ( Florea et al, 2019 ; Giannakakis et al, 2017 ; Huang et al, 2016 ) also address anxiety detection using machine learning techniques.…”

“…In terms of machine learning techniques used in text emotion detection, several notable studies are worth mentioning. For example, Ali et al (2022) uses learning techniques to classify psychotic disorders based on Facebook status updates. The random forest classifier outperformed competing classifiers.…”

Machine Learning for Depression Detection on Web and Social Media

“…These findings highlight the potential of analyzing social media data for detecting and understanding depression, providing insights into various factors associated with depressive symptoms and behaviors. Ali et al (2022) explored the classification of mental health issues in Facebook status updates using ML techniques. The focus of their study was primarily on the detection of depression, gradually expanding to six other common mental health issues: Anxiety, psychosis deviation, paranoia, unrealistic, and mild manic.…”

Applications of Hierarchical Classification Techniques for Classifying Anxiety Among Undergraduate Admission Candidates in Bangladesh

“…000 BDT) were more likely to exhibit anxiety, stress, and depression-related symptoms reported by Rabby et al(Rabby et al, 2023) which also coincided with our study ndings.The prior study for mental health prediction reported that in Sweden indicating non-signi cant superiority for the random forest model (AUC = 0.739, 95%CI 0.708-0.769)(Tate et al, 2020), in Pakistan where experimental evidence argues that RF outperforms its competitor classi ers(Ali et al, 2022), in India the most accurate one in Stacking technique based with an accuracy of prediction 81.75%(Vaishnavi et al, 2022). The previous studies in Bangladesh have reported that the RF model performed better in predicting stress and overall the lowest uncertainty estimates of those parameters, i.e.account individual and interaction in uences(Rahman et al, 2021).…”