Detecting Social Anxiety with Online Social Network Data

Chang, Ming-Yi; Tseng, Chih-Ying

doi:10.1109/mdm48529.2020.00073

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…Estos datos permiten que la información sea actual y que los conjuntos de datos sean de una mayor cantidad de usuarios. Chang y Tseng [10] recopilaron datos de entrenamiento y prueba, reclutando a más de 200 usuarios de redes sociales en línea, a los cuales les solicitaron llenar un cuestionario de escala. También rastrearon los datos de Facebook de los participantes para realizar el entrenamiento y prueba del algoritmo de aprendizaje automático.…”

Section: Recopilación De Datosunclassified

“…El trastorno de ansiedad es una enfermedad mental, que tiene el grupo más alto de carga social e individual de desórdenes mentales [2]. Aunque la ansiedad es uno de los trastornos más comunes, es muy poco detectado, lo cual evita que las personas reciban ayuda o tratamiento provocando síntomas severos [3]. Algunos estudios muestran que las personas que padecen este trastorno suelen ver las situaciones sociales como amenazantes, lo cual hace que puedan mostrar una preferencia por la interacción en línea sobre las interacciones cara a cara [4], [5].…”

Section: Introductionunclassified

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Detección de ansiedad mediante minería de texto en la era de redes sociales: Revisión bibliográfica

Torres

Moreta

2023

ESH

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Los trastornos mentales son cada vez más comunes, principalmente la ansiedad. Este trastorno, al no ser detectado a tiempo, puede volverse algo grave llevando a extremos como el suicidio. Sin embargo, dado que varias personas que lo padecen optan por la interacción en línea, existe la posibilidad de recurrir a la minería de texto enfocada a redes sociales. En este sentido, con el presente trabajo se buscó revisar la bibliografía que reporte estudios empleando minería de texto para determinar los usuarios que padecían de ansiedad mediante sus publicaciones o comentarios en sus redes sociales. La revisión se organizó a partir de las fases de minería de texto; es decir, recopilación de datos, preparación o preprocesamiento y clasificación. Entre los aspectos a resaltar están (i) la tendencia a utilizar una red social para obtener datos, especialmente Twitter; (ii) la relevancia de la limpieza de datos, aplicando técnicas como lemmatization; (iii) los algoritmos más destacados en la detección de ansiedad, como Naive Bayes, regresión logística, SVM y random forest. Más allá de los aportes de los trabajos revisados, se puede notar que persiste la necesidad de desarrollar más modelos que detecten el trastorno de interés.

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Section: Recopilación De Datosunclassified

Section: Introductionunclassified

Detección de ansiedad mediante minería de texto en la era de redes sociales: Revisión bibliográfica

Torres

Moreta

2023

ESH

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“…MTL model predicts a probable suicide attempt as well as the presence of abnormal mental health with 80% accuracy. In [23], authors proposed a machine learning-based approach (one-class SVM) to automatically identify a user from Anxiety disorder scale questionnaires with a social anxiety disorder by analyzing his/her posted content on social networks and one-class SVM achieves 0.794 F1-score. Saha et al [24] organized depression-related posts and comments from Facebook and Twitter into a concise word database and calculated sentiment levels for each instance.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

Ali¹,

Baqir²,

Sherazi³

et al. 2022

Computers, Materials &Amp; Continua

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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.

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“…Numerous studies have discussed the automatic detection of mental illnesses using social media data with varying focuses and methods, most of which used Natural Language Processing (NLP) approaches. Previous studies have primarily focused on understanding depression (Eichstaedt et al, 2018;Ríssola et al, 2019;Tadesse et al, 2019) and other mental illnesses such as anxiety disorder, schizophrenia, and PTSD (Bae et al, 2021;Chang & Tseng, 2020;Coppersmith et al, 2014). Various social media platforms, including Twitter, Facebook, and Reddit, have been considered.…”

Section: Introductionmentioning

confidence: 99%

“…Although a number of studies have addressed the topic of mental health disorder analysis, the focus has been limited to simple classification problems (Ríssola et al, 2022). The Multi-Class Single-Level (MCSL) classification algorithm is commonly used in the existing literature to classify a limited number of classes at a single level (Bae et al, 2021;Chang & Tseng, 2020;Coppersmith et al, 2014;Tadesse et al, 2019). These classes are typically binary, representing either an unhealthy or a healthy mental state.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media

Sutranggono,

Riyanarto Sarno,

Imam Ghozali

2024

JICT

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Mental health disorders pose a significant global public health challenge. Social media data provides insights into these conditions.Analysing text can help identify indications of mental health disorders through text-based analysis. However, despite the largenumber of studies on the analysis of mental health disorders, the predominant algorithm in the existing literature is the Multi-ClassSingle-Level (MCSL) classification algorithm, which is often used for simple classification tasks involving a limited number of classes.Typically, these classes are binary, representing either an unhealthy or a healthy mental state. This paper uses English text data from Reddit to classify mental health disorders. The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailedclassification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. Two different pre-processing scenarios were proposed to handle unstructured text data, one of the most challenging aspects of classifying text from social media. The results of the experiments show that the MCML classification algorithm successfully performs detailed classification and produces promising results for each classification level. The proposed pre-processing scenario influences the performance of each classifier and improves classification accuracy. The best accuracy results were obtained for the Robustly Optimised BERT Pre-training Approach (RoBERTa) classifier at level 1 and level 2 classifications, namely 0.98 and 0.85, respectively. Overall, the MCML classification algorithm is proven to be used as a benchmark for early detection of text-based mental health disorders.

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Detecting Social Anxiety with Online Social Network Data

Cited by 6 publications

References 14 publications

Detección de ansiedad mediante minería de texto en la era de redes sociales: Revisión bibliográfica

Detección de ansiedad mediante minería de texto en la era de redes sociales: Revisión bibliográfica

Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media

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