Depression and Suicide Prediction Using Natural Language Processing and Machine Learning

Kour, Harnain; Gupta, Manoj Kumar

doi:10.1007/978-981-16-8664-1_11

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…For Twitter/Facebook-based works, most existing ones (Kour and Gupta, 2022;Rabani et al, 2020;Sawhney et al, 2021;Sinha et al, 2019;) first collect posts by searching for specific suicide-related keywords suicide triggers. Indeed, spotting possible suicide triggers can provide unique and essential information for effective suicide prevention, enabling an integrated study involving multiple vital factors pertinent to suicide to be conducted, well before the exhibition of suicide ideation and its subsequent detection in contemporary works.…”

Section: Related Workmentioning

confidence: 99%

Suicide risk level prediction and suicide trigger detection: a benchmark dataset

Chen

Lin

et al. 2022

HKIE Transactions

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The increasing number of suicide events in recent years has set off an alarm in society. To prevent suicides, it is important to develop platforms with associated intelligent algorithms, such as those for early suicide ideation detection (SID). In general, entering people’s lives to obtain indicative information for effective SID is prohibitive due to the possible risk of privacy invasion. Social media posts provide valuable information about users’ activities, indicating important hints toward SID in a non-intrusive manner. Although multiple datasets have been collected from social media platforms for efficient SID, they either neglect many suicidal posts by searching for pre-defined keywords, or contain limited information about suicide ideation. In this paper, a newly collected dataset, which expands the coverage of suicidal posts with more fine-grained annotations of suicide risk levels and suicide triggers compared with existing datasets, is presented. Benchmarking results by a popular deep-learning model are analysed to validate the reliability and potential of the collected dataset. Furthermore, multiple application scenarios of the dataset are discussed. The proposed dataset is expected to enhance the research on suicide ideation and behaviours, and have a strong impact on SID and more importantly, suicide prevention to save invaluable lives.

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Section: Related Workmentioning

confidence: 99%

Suicide risk level prediction and suicide trigger detection: a benchmark dataset

Chen

Lin

et al. 2022

HKIE Transactions

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A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges

Montejo-Ráez,

Molina-González,

Jiménez-Zafra

et al. 2024

Computer Science Review

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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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Depression and Suicide Prediction Using Natural Language Processing and Machine Learning

Cited by 3 publications

References 23 publications

Suicide risk level prediction and suicide trigger detection: a benchmark dataset

Suicide risk level prediction and suicide trigger detection: a benchmark dataset

A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges

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

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