On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data

Uban, Ana Sabina; Chulvi, Berta; Rosso, Paolo

doi:10.1007/978-3-030-80599-9_27

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…El 100% de los documentos incluidos en este estudio se encuentra publicado en inglés. Por su parte, para el análisis de datos, algunas investigaciones utilizaron textos en su lengua materna (Katchapakirin et al, 2018, Eldin et al, 2019Priya Sri et al, 2021), otros usan textos en su lengua materna traducidos al inglés (López Úbeda et al, 2019;Mehedy et al, 2021;Chatrinan et al, 2021), y en otros, este dato no se indicaban específicamente en los estudios (Wang et al, 2020;Ricard & Hassanpour, 2021;Sabina et al, 2021;Ragheb et al, 2021) (Figura 4).…”

Section: Años De Publicación De Los Artículos País De Origen E Idioma...unclassified

Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios

Mieles Toloza,

Delgado Meza

2024

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Las enfermedades mentales constituyen una de las principales causas de angustia en la vida de las personas a nivel individual, y repercuten en la salud y el bienestar de la sociedad. Para captar estas complejas asociaciones, las ciencias computacionales y la comunicación, a través del uso de métodos de procesamiento del lenguaje natural (NLP) en datos recolectados en redes sociales, han aportado prometedores avances para potenciar la atención sanitaria mental proactiva y ayudar al diagnóstico precoz. Por ello, se realizó una revisión sistemática de la literatura acerca de la detección de alteraciones mentales a través de redes sociales, mediante el uso de NLP en los últimos 5 años, que permitió identificar métodos, tendencias y orientaciones futuras, a través del análisis de 73 estudios, de 509 que arrojó la revisión de documentos extraídos de bases de datos científicas. El estudio reveló que, los fenómenos más comúnmente estudiados, correspondieron a Depresión e Ideación suicida, identificados a través del uso de algoritmos como el LIWC, CNN, LSTM, RF y SVM, en datos extraídos principalmente de Reddit y Twitter. Este estudio, finalmente proporciona algunas recomendaciones sobre las metodologías de NLP para la detección de enfermedades mentales, que pueden ser adoptadas en el ejercicio de profesionales interesados en la salud mental, y algunas reflexiones sobre el uso de estas tecnologías.

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Section: Años De Publicación De Los Artículos País De Origen E Idioma...unclassified

Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios

Mieles Toloza,

Delgado Meza

2024

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“…Now a days the cost of detecting or diagnosing mental illness have improved so by that people can't invest more on that and that cases remain undetected for that the author in this used social media as a platform to detect mental illness [1] This makes the claim that user personality profiles can be used to tailor content, optimize campaigns, and enhance online advertising. [2] They present analysis of 2,34,735 supporter messages is to discover different strategies which correspond with clinical outcomes [3] Non-linear methods for regression was applied for achieving strong correlation between predicted and actual user income [4] Here survey responses and status updates from 24,904 Face book are used to develop a regression model that predicts users degree of depression based on their Face book status update [5] The author in this detect and refer the user's as soon as possible to professional help so they used BoSE representation which represent social media documents by a set of fine grained emotions automatically using a lexical resource [6] social media provides a great opportunity to increase the available data to researchers for better information in health field for a new approach is done by this the data is gathered quickly and cheaply and inform the necessary ethical discussion [7] Eating disorder are complex in mental disorders and they are responsible for highest mortality rate among mental illness for this a snowball sampling method is developed for automatically gathering of individuals who self identify as eating disorder. He are a new findings of how an eating disorder community develops on social media [8] They examine the potential for posting of social media which is a new type of lens in understanding depression.…”

Section: Literature Surveymentioning

confidence: 99%

A New Model That Figures out Sub-Emotionality and the Changes in It over Time

Devi

Rajesh

2023

Advances in Transdisciplinary Engineering

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On Earth, there are millions of people, and everyone of them has emotions that they exhibit through their behaviors and conduct. Some emotions have a negative impact on human functioning and can lead to mental diseases that can deteriorate over time if they are not diagnosed early. Identifying these mental difficulties is a difficult process that is important in order to provide them with assistance in fixing the issue before it develops. Observing the manner in which individuals express their emotions in either writing or social media conversations is one such method. In the study, we evaluate two mathematical algorithms that imitate social media user activity and emotional fluctuations. In this review, we utilize two datasets for widespread mental disorders. Anorexia with depression. The results reveal the existence and variety of emotions exhibited by individuals on social media with depression and anorexia through the use of two visualizations that highlight pertinent information.

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“…The development of fair AI technologies in mental healthcare supports unbiased clinical decision-making. Moreover, interpretation [70], [71] and explanation [72], [73] are possible means for detecting bias so that it may be addressed. Furthermore, healthcare practitioners and researchers must collectively ensure effective evaluation mechanisms of AI technologies for mental health in support of trustworthy and fair decision-making.…”

Section: Ethical Considerationsmentioning

confidence: 99%

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Garg¹,

Saxena²,

Naseem³

et al. 2023

Preprint

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<p>Interactions among humans on social media often convey intentions behind their actions, yielding a psychological language resource for Mental Health Analysis (MHA) of online users. The success of Computational Intelligence Techniques (CIT) for inferring mental illness from such social media resources points to NLP as a lens for causal analysis and perception mining. However, we argue that more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare. To bridge this gap, we posit two significant dimensions: (1) Causal analysis to illustrate a cause and effect relationship in the user generated text; (2) Perception mining to infer psychological perspectives of social effects on online users intentions. Within the scope of Natural Language Processing (NLP), we further explore critical areas of inquiry associated with these two dimensions, specifically through recent advancements in discourse analysis. This position paper guides the community to explore solutions in this space and advance the state of practice in developing conversational agents for inferring mental health from social media. We advocate for a more explainable approach toward modeling computational psychology problems through the lens of language as we observe an increased number of research contributions in dataset and problem formulation for causal relation extraction and perception enhancements while inferring mental states.</p>

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On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data

Cited by 8 publications

References 17 publications

Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios

Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios

A New Model That Figures out Sub-Emotionality and the Changes in It over Time

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

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