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 | |
DOI | 10.2196/24361 |
发表期刊 | JOURNAL OF MEDICAL INTERNET RESEARCH |
ISSN | 1438-8871 |
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 | |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
The Hidden Pandemic (321KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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