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The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets

BACKGROUND: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. O...

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Detalles Bibliográficos
Autores principales: Xue, Jia, Chen, Junxiang, Chen, Chen, Hu, Ran, Zhu, Tingshao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652592/
https://www.ncbi.nlm.nih.gov/pubmed/33108315
http://dx.doi.org/10.2196/24361
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author Xue, Jia
Chen, Junxiang
Chen, Chen
Hu, Ran
Zhu, Tingshao
author_facet Xue, Jia
Chen, Junxiang
Chen, Chen
Hu, Ran
Zhu, Tingshao
author_sort Xue, Jia
collection PubMed
description BACKGROUND: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. OBJECTIVE: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. METHODS: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. RESULTS: We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence–related news (eg, Tara Reade, Melissa DeRosa). CONCLUSIONS: This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks.
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spelling pubmed-76525922020-11-13 The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets Xue, Jia Chen, Junxiang Chen, Chen Hu, Ran Zhu, Tingshao J Med Internet Res Original Paper BACKGROUND: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. OBJECTIVE: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. METHODS: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. RESULTS: We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence–related news (eg, Tara Reade, Melissa DeRosa). CONCLUSIONS: This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks. JMIR Publications 2020-11-06 /pmc/articles/PMC7652592/ /pubmed/33108315 http://dx.doi.org/10.2196/24361 Text en ©Jia Xue, Junxiang Chen, Chen Chen, Ran Hu, Tingshao Zhu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.11.2020. https://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Original Paper
Xue, Jia
Chen, Junxiang
Chen, Chen
Hu, Ran
Zhu, Tingshao
The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
title The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
title_full The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
title_fullStr The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
title_full_unstemmed The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
title_short The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
title_sort hidden pandemic of family violence during covid-19: unsupervised learning of tweets
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652592/
https://www.ncbi.nlm.nih.gov/pubmed/33108315
http://dx.doi.org/10.2196/24361
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