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Detection of suicidal ideation on social media: multimodal, relational, and behavioral analysis

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dc.contributor.author Ramírez Cifuentes, Diana
dc.contributor.author Freire, Ana
dc.contributor.author Baeza Yates, Ricardo
dc.contributor.author Puntí, Joaquim
dc.contributor.author Medina Bravo, Pilar, 1966-
dc.contributor.author Alejandro Velazquez, Diego
dc.contributor.author Gonfaus, Josep Maria
dc.contributor.author Gonzàlez, Jordi
dc.date.accessioned 2020-10-28T07:39:03Z
dc.date.available 2020-10-28T07:39:03Z
dc.date.issued 2020
dc.identifier.citation Ramírez-Cifuentes D, Freire A, Baeza-Yates R, Puntí J, Medina-Bravo P, Velazquez DA, Gonfaus JM, Gonzàlez J. Detection of suicidal ideation on social media: multimodal, relational, and behavioral analysis. J Med Internet Res. 2020 Jul 7;22(7):e17758. DOI: 10.2196/17758
dc.identifier.issn 1439-4456
dc.identifier.uri http://hdl.handle.net/10230/45595
dc.description.abstract Background: 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. Objective: 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. Methods: 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). Results: 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. Conclusions: 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.
dc.description.sponsorship This study was supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Program (MDM-2015-0502). The authors acknowledge the funding received by the Spanish project TIN2015-65464-R (MINECO/FEDER). The authors also acknowledge the support of NVIDIA Corporation for the donation of a Tesla K40 GPU and a GTX TITAN GPU, used for this research.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher JMIR Publications
dc.relation.ispartof Journal of medical Internet research. 2020 Jul 7;22(7):e17758
dc.rights © Diana Ramírez-Cifuentes, Ana Freire, Ricardo Baeza-Yates, Joaquim Puntí, Pilar Medina-Bravo, Diego Alejandro Velazquez, Josep Maria Gonfaus, Jordi Gonzàlez. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.07.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Detection of suicidal ideation on social media: multimodal, relational, and behavioral analysis
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.2196/17758
dc.subject.keyword Social media
dc.subject.keyword Mental health
dc.subject.keyword Suicidal ideation
dc.subject.keyword Risk assessment
dc.subject.keyword Machine learning
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65464-R
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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