The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
文献类型：期刊论文
| 作者 | Xue, Jia2,3; Chen, Junxiang1; Chen, Chen5 ; Hu, Ran3; Zhu, Tingshao4
|
| 刊名 | JOURNAL OF MEDICAL INTERNET RESEARCH
 |
| 出版日期 | 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 |
| DOI | 10.2196/24361 |
| 通讯作者 | Xue, Jia(jia.xue@utoronto.ca) |
| 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 |
| WOS记录号 | WOS:000589257200004 |
| 出版者 | JMIR PUBLICATIONS, INC |
| 源URL | [http://ir.psych.ac.cn/handle/311026/33522]  |
| 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.Univ Pittsburgh, Sch Med, Pittsburgh, PA USA 2.Univ Toronto, Fac Informat, Toronto, ON, Canada 3.Univ Toronto, Factor Inwentash Fac Social Work, 246 Bloor St W, Toronto, ON M5S 1V4, Canada 4.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 5.Univ Toronto, Middleware Syst Res Grp, Toronto, ON, 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. |
入库方式： OAI收割
来源：心理研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。