Predicting Mental Illness using Social Media Posts and Comments

kamal, Mohsin; Khan, Saif Ur Rehman; Hussain, Shahid; Nasir, Anam; Aslam, Khurram; Tariq, Subhan; Ullah, Mian Farhan

doi:10.14569/ijacsa.2020.0111271

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…The data analysis was carried out with the use of novel machine learning techniques, particularly, GBTs and SHAP values. Recent studies showed that the GBT algorithm is the best performing algorithm in the prediction of various mental health problems [68][69][70][71][72][73][74][75][76][77][78][79][80][81].…”

Section: Discussionmentioning

confidence: 99%

“…Extreme gradient boosting (XGBoost) has been widely applied in various aspects, such as credit scoring, transportation or even medicine (for instance, for cancer morbidity prediction). Several recent studies show that GBT is the best performing algorithm in the prediction of various aspects, such as the prediction of suicide, psychological health, stress, mental health problems or depression [68][69][70][71][72][73][74][75][76][77][78][79][80][81]. Sanderson et al [68] claim that "the gradient boosted trees model class appeared to be the most promising model class for future research".…”

Section: Methodsmentioning

confidence: 99%

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Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries

Majcherek

Kowalski

Lewandowska

2022

IJERPH

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Ensuring the health and well-being of workers should be a top priority for employers and governments. The aim of the article is to evaluate and rank the importance of mental health determinants: lifestyle, demographic factors and socio-economic status. The research study is based on EHIS 2013–2015 data for a sample of N = 140,791 employees from 30 European countries. The results obtained using machine learning techniques such as gradient-boosted trees and SHAPley values show that the mental health of European employees is strongly determined by the BMI, age and social support from close people. The next vital features are alcohol consumption, an unmet need for health care and sports activity, followed by the affordability of medicine or treatment, income and occupation. The wide range of variables clearly indicates that there is an important role for governments to play in order to minimize the risk of mental disorders across various socio-economic groups. It is also a signal for businesses to help boost the mental health of their employees by creating holistic, mentally friendly working conditions, such as offering time-management training, implementing morning briefings, offering quiet areas, making employees feel valued, educating them about depression and burnout symptoms, and promoting a healthy lifestyle.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries

Majcherek

Kowalski

Lewandowska

2022

IJERPH

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“…Nasir et al [14] proposed a methodology for identifying mental illness via their communication on social media networks. Social media is an excellent source of communication and interaction among people.…”

Section: A Related Workmentioning

confidence: 99%

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Ananthapadmanabha¹,

C²,

Sabariraju³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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One in every four individuals has a diagnosable mental disease in a year. Around 20% of children and adolescents have a mental health condition and often ignore it. It is found that 93% of youth use social media to communicate and engage, as it reflects their emotions, moods, and thoughts. As a result, machine learning algorithms may anticipate people's moods and emotions based on their postings and comments. On the other hand, psychometric tests use questions to determine how individuals think, feel, behave, and react. It is necessary to investigate a hybrid approach for identifying people's mental illness by combining social media inputs and psychometric tests, especially during a pandemic. The hybrid approach can combine the results from both the models to reflect on the user's digital & non-digital reactions to certain sensitive situations to determine their mental state. Hence, the present paper aims to develop a web framework that can forecast the emergence of mental illness in the future based on data from social media comments and real-time data from psychometric tests using machine learning algorithms. The proposed work includes the cluster ensemble method for social media posts and a convolution neural network model for psychometric tests. This model predicts mental illness with an accuracy of 87.05 percent. The individual can use this result to take the required precautions by visiting a psychologist.

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“…2017; Nasir et al . 2020). Therefore, we need to think carefully about the potential impact from NLP to the published results, especially in clinical settings.…”

Section: Commonly Used Text Preprocessing Practicesmentioning

confidence: 99%

“…A better practice is to be clear and consistent with the definitions of these terms in the tokenization process. Most existing literature does not specify the tokenization methods in detail, including some papers with actual implications for diagnosis of serious neurological and psychiatric disorders (Posada et al 2017;Nasir et al 2020). Therefore, we need to think carefully about the potential impact from NLP to the published results, especially in clinical settings.…”

Section: Tokenizationmentioning

confidence: 99%

Comparison of text preprocessing methods

Chai

2022

Nat. Lang. Eng.

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Text preprocessing is not only an essential step to prepare the corpus for modeling but also a key area that directly affects the natural language processing (NLP) application results. For instance, precise tokenization increases the accuracy of part-of-speech (POS) tagging, and retaining multiword expressions improves reasoning and machine translation. The text corpus needs to be appropriately preprocessed before it is ready to serve as the input to computer models. The preprocessing requirements depend on both the nature of the corpus and the NLP application itself, that is, what researchers would like to achieve from analyzing the data. Conventional text preprocessing practices generally suffice, but there exist situations where the text preprocessing needs to be customized for better analysis results. Hence, we discuss the pros and cons of several common text preprocessing methods: removing formatting, tokenization, text normalization, handling punctuation, removing stopwords, stemming and lemmatization, n-gramming, and identifying multiword expressions. Then, we provide examples of text datasets which require special preprocessing and how previous researchers handled the challenge. We expect this article to be a starting guideline on how to select and fine-tune text preprocessing methods.

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Predicting Mental Illness using Social Media Posts and Comments

Cited by 13 publications

References 22 publications

Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries

Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries

Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Comparison of text preprocessing methods

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