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The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
Xue, Jia1,2; Chen, Junxiang3; Chen, Chen4; Hu, Ran1; Zhu, Tingshao5
第一作者Xue, Jia
通讯作者邮箱jia.xue@utoronto.ca
心理所单位排序5
摘要

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.

关键词Twitter family violence COVID-19 machine learning big data infodemiology infoveillance
2020-11-06
DOI10.2196/24361
发表期刊JOURNAL OF MEDICAL INTERNET RESEARCH
ISSN1438-8871
卷号22期号:11页码:11
期刊论文类型实证研究
收录类别SCI
出版者JMIR PUBLICATIONS, INC
WOS关键词INTIMATE PARTNER VIOLENCE ; DOMESTIC VIOLENCE ; HEALTH
WOS研究方向Health Care Sciences & Services ; Medical Informatics
WOS类目Health Care Sciences & Services ; Medical Informatics
WOS记录号WOS:000589257200004
WOS分区Q1
引用统计
被引频次:51[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/33522
专题社会与工程心理学研究室
通讯作者Xue, Jia
作者单位1.Univ Toronto, Factor Inwentash Fac Social Work, 246 Bloor St W, Toronto, ON M5S 1V4, Canada
2.Univ Toronto, Fac Informat, Toronto, ON, Canada
3.Univ Pittsburgh, Sch Med, Pittsburgh, PA USA
4.Univ Toronto, Middleware Syst Res Grp, Toronto, ON, Canada
5.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Xue, Jia,Chen, Junxiang,Chen, Chen,et al. The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets[J]. JOURNAL OF MEDICAL INTERNET RESEARCH,2020,22(11):11.
APA Xue, Jia,Chen, Junxiang,Chen, Chen,Hu, Ran,&Zhu, Tingshao.(2020).The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.JOURNAL OF MEDICAL INTERNET RESEARCH,22(11),11.
MLA Xue, Jia,et al."The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets".JOURNAL OF MEDICAL INTERNET RESEARCH 22.11(2020):11.
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