Welcome to the UPF Digital Repository

Detecting signs of depression in tweets in spanish: behavioral and linguistic analysis

Show simple item record

dc.contributor.author Leis Machin, Angela
dc.contributor.author Ronzano, Francesco
dc.contributor.author Mayer, Miguel Ángel, 1960-
dc.contributor.author Furlong, Laura I., 1971-
dc.contributor.author Sanz, Ferran
dc.date.accessioned 2019-07-08T07:40:21Z
dc.date.available 2019-07-08T07:40:21Z
dc.date.issued 2019
dc.identifier.citation Leis A, Ronzano F, Mayer MA, Furlong LI, Sanz F. Detecting signs of depression in tweets in spanish: behavioral and linguistic analysis. J Med Internet Res. 2019; 21(6):e14199. DOI 10.2196/14199
dc.identifier.issn 1438-8871
dc.identifier.uri http://hdl.handle.net/10230/41957
dc.description.abstract Background: Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people’s daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English. Objective: The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression. Methods: This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out. Results: In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001). Conclusions: Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.
dc.description.sponsorship We received support from the Agency for Management of University and Research Grants in Catalonia (Spain) for the incorporation of new research personnel (FI2016), the European Union H2020 Research and Innovation Programme 2014-2020 under grant agreement number 634143 (MedBioinformatics: Creating medically driven integrative bioinformatics applications focused on oncology and central nervous system disorders and their comorbidities). The Research Programme on Biomedical Informatics is a member of the Spanish National Bioinformatics Institute, PRB2-ISCIII, and it is supported by grant PT17/0009/0014.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher JMIR Publications
dc.relation.ispartof J Med Internet Res. 2019; 21(6):e14199
dc.rights © Angela Leis, Francesco Ronzano, Miguel A Mayer, Laura I Furlong, Ferran Sanz. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.06.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
dc.title Detecting signs of depression in tweets in spanish: behavioral and linguistic analysis
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.2196/14199
dc.subject.keyword Depression
dc.subject.keyword Mental health
dc.subject.keyword Social media
dc.subject.keyword Text mining
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/634143
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account


Compliant to Partaking