Google Scholar: cites
Detection of Suicidal Ideation on Social Media : Multimodal, Relational, and Behavioral Analysis
Ramírez-Cifuentes, Diana (Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions)
Freire, Ana (Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions)
Baeza-Yates, Ricardo (Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions)
Puntí Vidal, Joaquim (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Medina-Bravo, Pilar (Universitat Pompeu Fabra. Departament de Comunicació)
Velazquez, Diego Alejandro (Centre de Visió per Computador (Bellaterra, Catalunya))
Gonfaus, Josep (Visual Tagging Services)
Gonzàlez, Jordi (Centre de Visió per Computador (Bellaterra, Catalunya))
Universitat Autònoma de Barcelona. Departament de Psicologia Clínica i de la Salut

Data: 2020
Resum: Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P =. 04) and median tweet length (P =. 04) were significantly different. The median number of friends for a focused control user (median 578. 5) was higher than that for a user at risk (median 372. 0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Social media ; Mental health ; Suicidal ideation ; Risk assessment ; Machine learning
Publicat a: Journal of medical Internet research, Vol. 22 (july 2020) , ISSN 1438-8871

DOI: 10.2196/17758
DOI: 10.2196/preprints.17758
PMID: 32673256

16 p, 348.8 KB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut d’Investigació i Innovació Parc Taulí (I3PT)
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 Registre creat el 2022-02-07, darrera modificació el 2024-02-29

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