In Journal of medical Internet research ; h5-index 88.0
BACKGROUND : Family violence (including IPV/domestic violence, child abuse, elder abuse) is the hidden pandemics during the COVID-19. The rates of family violence are rising fast. Women and children are disproportionately affected and vulnerable during the pandemic.
OBJECTIVE : This study aims to provide a large-scale analysis of public discourse mentioning family violence and the COVID-19 pandemic on Twitter.
METHODS : We analyzed one million Tweets related to family violence and COVID-19 from April 12 to July 16, 2020, for this study. We used the machine learning approach, Latent Dirichlet Allocation, and identified salient themes, topics, and representative Twitter examples.
RESULTS : We extracted nine themes from what people are saying about family violence, and the COVID-19 pandemic, including (1) Increased vulnerability: COVID-19 and family violence (e.g., rising rates, hotline calls increased, murder & homicide); (2) the types of family violence (e.g., child abuse, domestic violence, sexual abuse) and (3) forms of family violence (e.g., physical aggression, coercive control); (4) risk factors of family violence (e.g., alcohol abuse, financial constraints, gun, quarantine); (5) victims of family violence (e.g., LGBTQ, women, and women of color, children); (6) social services for family violence (e.g., hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (e.g., 911 calls, police arrest, protective orders, abuse reports); (8) Social movement/ awareness (e.g., support victims, raise awareness); and (9) domestic violence-related news (e.g., Tara Reade, Melissa Derosa).
CONCLUSIONS : This study overcomes the limitation of existing scholarship that lacks data for consequences of COVID-19 on family violence. We contribute to understanding family violence during the pandemic by providing surveillance in Tweets, which is essential to identifying potentially useful policy programs in offering targeted support for victims and survivors and preparing for the next wave.
Xue Jia, Chen Junxiang, Chen Chen, Hu Ran, Zhu Tingshao