| The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets | |
Xue, Jia1,2; Chen, Junxiang3; Chen, Chen4 ; Hu, Ran1; Zhu, Tingshao5
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| Corresponding Author | Xue, Jia(jia.xue@utoronto.ca) |
| Abstract | 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. |
| Keyword | Twitter family violence COVID-19 machine learning big data infodemiology infoveillance |
| 2020-11-06 | |
| Language | 英语 |
| DOI | 10.2196/24361 |
| Source Publication | JOURNAL OF MEDICAL INTERNET RESEARCH
 |
| ISSN | 1438-8871 |
| Volume | 22Issue:11Pages:11 |
| Indexed By | SCI |
| Publisher | JMIR PUBLICATIONS, INC |
| WOS Keyword | INTIMATE PARTNER VIOLENCE ; DOMESTIC VIOLENCE ; HEALTH |
| WOS Research Area | Health Care Sciences & Services ; Medical Informatics |
| WOS Subject | Health Care Sciences & Services ; Medical Informatics |
| WOS ID | WOS:000589257200004 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.psych.ac.cn/handle/311026/33522 |
| Collection | 社会与工程心理学研究室 |
| Corresponding Author | Xue, Jia |
| Affiliation | 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 |
| Recommended Citation 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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