A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

Alqazzaz, Ali; Quasim, Mohammad Tabrez; Alshahrani, Mohammed Mujib; Alrashdi, Ibrahim; Khan, Mohammad Ayoub

doi:10.32604/csse.2023.031048

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Twitter is the most common source of data used in the studies. When it comes to ML methods, SVM [8, 26] and naive Bayes classi er [18,22] are the most used methods under traditional methods, while in deep learning, LSTM [12,16,21,25] and CNN [8] are the popular methods, with BERT, Fasttext, and ELMO as the embeddings. Figure 18 shows a mind map around various essential components and concepts of DAD detection in text.…”

Section: Modalities Data Sources ML Techniques and Other Aspects In D...mentioning

confidence: 99%

Review and Content Analysis of Textual Expressions as a Marker for Depressive and Anxiety Disorders (DAD) Detection Using Machine Learning

Sharma,

Damani,

Chariar

2023

Preprint

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Background Depressive disorders (including major depressive disorder and dysthymia) and anxiety (generalized anxiety disorder or GAD) disorders are the two most prevalent mental illnesses. Early diagnosis of these afflictions can lead to cost-effective treatment with a better outcome prospectus. With the advent of digital technology and platforms, people express themselves by various means, such as social media posts, blogs, journals, instant messaging services, etc. Text remains the most common and convenient form of expression. Therefore, it can be used to predict the onset of anxiety and depression. Methods Scopus and Web of Science (WoS) databases were used to retrieve the relevant literature using a set of predefined search strings. Irrelevant publications were filtered using multiple criteria. The research meta data was subsequently analyzed using the Biblioshiny Tool of R. Finally, a comparative analysis of most suitable documents is presented. Results A total of 103 documents were used for bibliometric mapping in terms of research outcome over the past years, productivity of authors, institutions, and countries, collaborations, trend topics, keyword co-occurrence, etc. Neural networks and support vector machines are the most popular ML techniques; word embeddings are extensively used for text representations. There is a shift toward using multiple modalities. SVM, Naive Bayes, and LSTM are the most commonly used ML methods; social media is the most commonly used source of data (Twitter is the most common platform); and audio is the most commonly used modality that is combined with text for depressive and anxiety disorders (DAD) detection. Conclusions Asian countries are leading in the research output with China and India being the top countries in terms of the number of research publications. However, more international collaborations are needed. Limited research exists for anxiety disorders. Co-occurrence of anxiety and depressive disorders is high (33% of studies).

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Section: Modalities Data Sources ML Techniques and Other Aspects In D...mentioning

confidence: 99%

Review and Content Analysis of Textual Expressions as a Marker for Depressive and Anxiety Disorders (DAD) Detection Using Machine Learning

Sharma,

Damani,

Chariar

2023

Preprint

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Review and content analysis of textual expressions as a marker for depressive and anxiety disorders (DAD) detection using machine learning

Sharma,

Damani,

Chariar

2023

Discov Artif Intell

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Depressive disorders (including major depressive disorder and dysthymia) and anxiety (generalized anxiety disorder or GAD) disorders are the two most prevalent mental illnesses. Early diagnosis of these afflictions can lead to cost-effective treatment with a better outcome prospectus. With the advent of digital technology and platforms, people express themselves by various means, such as social media posts, blogs, journals, instant messaging services, etc. Text remains the most common and convenient form of expression. Therefore, it can be used to predict the onset of anxiety and depression. Scopus and Web of Science (WoS) databases were used to retrieve the relevant literature using a set of predefined search strings. Irrelevant publications were filtered using multiple criteria. The research meta data was subsequently analyzed using the Biblioshiny Tool of R. Finally, a comparative analysis of most suitable documents is presented. A total of 103 documents were used for bibliometric mapping in terms of research outcome over the past years, productivity of authors, institutions, and countries, collaborations, trend topics, keyword co-occurrence, etc. Neural networks and support vector machines are the most popular ML techniques; word embeddings are extensively used for text representations. There is a shift toward using multiple modalities. SVM, Naive Bayes, and LSTM are the most used ML methods; social media is the most used source of data (Twitter is the most common platform); and audio is the most used modality that is combined with text for depressive and anxiety disorders (DAD) detection. Text data provides good cues for the detection of DAD using machine learning. However, the findings in most of the cases are based on a limited amount of data. Using large amounts of data with other modalities can help develop more generalized DAD-detection systems. Asian countries are leading in the research output with China and India being the top countries in terms of the number of research publications. However, more international collaborations are needed. Limited research exists for anxiety disorders. Co-occurrence of anxiety and depressive disorders is high (33% of studies).

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A Comprehensive Analysis of Psychiatric Disorders Using Deep Learning

Manickam,

Ponnusamy

2023

2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)

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A Deep Learning Model to Analyse Social-Cyber Psychological Problems in Youth

Cited by 4 publications

References 17 publications

Review and Content Analysis of Textual Expressions as a Marker for Depressive and Anxiety Disorders (DAD) Detection Using Machine Learning

Review and Content Analysis of Textual Expressions as a Marker for Depressive and Anxiety Disorders (DAD) Detection Using Machine Learning

Review and content analysis of textual expressions as a marker for depressive and anxiety disorders (DAD) detection using machine learning

A Comprehensive Analysis of Psychiatric Disorders Using Deep Learning

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