A Survey on Mental Health Detection in Online Social Network

Rahman, Rohizah Abd; Omar, Khairuddin; Noah, Shahrul Azman Mohd; Danuri, Mohd Shahrul Nizam Mohd

doi:10.18517/ijaseit.8.4-2.6830

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…We chose naive Bayes (NB), decision tree, logistic regression (LR), random forest, and support vector machine [ 5 , 37 ] as classification methods to detect users with depression. From model performance, we obtained a quick picture about the importance of our lexicon.…”

Section: Resultsmentioning

confidence: 99%

“…The traditional clinical diagnosis of depression mainly relies on standardized assessments, which are highly accurate but have limitations in detection efficiency [ 5 ]. The medical diagnosis requires not only filling in a depression assessment scale, such as the Self-rating Depression Scale, but also a one-to-one interview and long-term observation [ 6 ], which involve high costs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

Li¹,

Li²,

Huang³

et al. 2020

JMIR Med Inform

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Background According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. Objective The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. Methods We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. Results The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. Conclusions Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

Li¹,

Li²,

Huang³

et al. 2020

JMIR Med Inform

View full text Add to dashboard Cite

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“…The World Health Organization (WHO) has defined "mental health" as the condition of a person who is able to handle his/her stress in life according to his/her ability, but is still able to work normally and productively as well as contribute to the society [1]. Factors that affect mental health probably originate from an individual's way of life, such as work stress, bad financial situation, family issues, relationship problems, and violence, along with environmental factors [2]. These situations can contribute to mental health disorders, such as depression, anxiety, stress, and various psychological disorders that exert an impact on the quality of life and holistic well-being of a person.…”

Section: Introductionmentioning

confidence: 99%

“…OSNs have become popular in recent years and have provided a new medium to communicate and share information. OSNs are used regularly by millions of people worldwide [2]. Through OSNs, users can express their feelings and thoughts by posting different types of data (e.g., text, images, videos, and audios) regarding their daily activities.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Methods in Mental Health Detection: A Systematic Review

et al. 2020

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This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new data set extracted from different OSNs sources. The extracted data were examined using a statistical analysis or machine learning techniques. Several studies also applied multimethod techniques, which included distributing questionnaires while requesting for the respondents' consent to later access and extract information from his/her OSNs account. Big data in OSNs contribute on mental health problem detection. The presented method is an alternative approach to the early detection of mental health problems rather than using traditional strategies, such as collecting data through questionnaires or devices and sensors, which are time-consuming and costly. However, mental health problem detection through OSNs necessitates a comprehensive adoption, innovative algorithms, and computational linguistics to describe its limitations and challenges. Moreover, referrals from mental health specialists as subject matter experts are also required to help obtain accurate and effective information.

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