Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics

Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open‐source digital conversatio...

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Autores principales:	Ruby Castilla-Puentes, Anjali Dagar, Dinorah Villanueva, Laura Jimenez-Parrado, Liliana Gil Valleta, Tatiana Falcone
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Depression Hispanics Artificial intelligence Latinos Big data Machine learning Psychiatry RC435-571
Acceso en línea:	https://doaj.org/article/de8fdde2ee6148d295160af0b7bdc5e7
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spelling	oai:doaj.org-article:de8fdde2ee6148d295160af0b7bdc5e72021-12-05T12:16:28ZDigital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics10.1186/s12991-021-00372-01744-859Xhttps://doaj.org/article/de8fdde2ee6148d295160af0b7bdc5e72021-11-01T00:00:00Zhttps://doi.org/10.1186/s12991-021-00372-0https://doaj.org/toc/1744-859XAbstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open‐source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. In this cross-sectional study, only unique posts were included in the analysis and were primarily analyzed for their tone, topic, and attitude towards depression between the two groups using descriptive statistical tools. Results A total of 441,000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half‐million digital conversations about depression using machine learning, we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.Ruby Castilla-PuentesAnjali DagarDinorah VillanuevaLaura Jimenez-ParradoLiliana Gil ValletaTatiana FalconeBMCarticleDepressionHispanicsArtificial intelligenceLatinosBig dataMachine learningPsychiatryRC435-571ENAnnals of General Psychiatry, Vol 20, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Depression Hispanics Artificial intelligence Latinos Big data Machine learning Psychiatry RC435-571
spellingShingle	Depression Hispanics Artificial intelligence Latinos Big data Machine learning Psychiatry RC435-571 Ruby Castilla-Puentes Anjali Dagar Dinorah Villanueva Laura Jimenez-Parrado Liliana Gil Valleta Tatiana Falcone Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics
description	Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open‐source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. In this cross-sectional study, only unique posts were included in the analysis and were primarily analyzed for their tone, topic, and attitude towards depression between the two groups using descriptive statistical tools. Results A total of 441,000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half‐million digital conversations about depression using machine learning, we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.
format	article
author	Ruby Castilla-Puentes Anjali Dagar Dinorah Villanueva Laura Jimenez-Parrado Liliana Gil Valleta Tatiana Falcone
author_facet	Ruby Castilla-Puentes Anjali Dagar Dinorah Villanueva Laura Jimenez-Parrado Liliana Gil Valleta Tatiana Falcone
author_sort	Ruby Castilla-Puentes
title	Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics
title_short	Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics
title_full	Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics
title_fullStr	Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics
title_full_unstemmed	Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics
title_sort	digital conversations about depression among hispanics and non-hispanics in the us: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in hispanics
publisher	BMC
publishDate	2021
url	https://doaj.org/article/de8fdde2ee6148d295160af0b7bdc5e7
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Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics

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