The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
Xue, Jia2,5; Chen, Junxiang3; Chen, Chen4; Hu, Ran5; Zhu, Tingshao1
刊名JOURNAL OF MEDICAL INTERNET RESEARCH
2020-11-06
卷号22期号:11页码:11
关键词Twitter family violence COVID-19 machine learning big data infodemiology infoveillance
ISSN号1438-8871
DOI10.2196/24361
产权排序5
文献子类实证研究
英文摘要

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.

WOS关键词INTIMATE PARTNER VIOLENCE ; DOMESTIC VIOLENCE ; HEALTH
WOS研究方向Health Care Sciences & Services ; Medical Informatics
语种英语
出版者JMIR PUBLICATIONS, INC
WOS记录号WOS:000589257200004
内容类型期刊论文
源URL[http://ir.psych.ac.cn/handle/311026/33521]  
专题心理研究所_社会与工程心理学研究室
通讯作者Xue, Jia
作者单位1.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
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.Univ Toronto, Factor Inwentash Fac Social Work, 246 Bloor St W, Toronto, ON M5S 1V4, Canada
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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