Abstract

BackgroundFamily 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.ObjectiveThis study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter.MethodsWe 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.ResultsWe 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).ConclusionsThis 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.

Highlights

  • As seen in the case of Ebola, epidemics increase the rates of domestic violence [1]

  • This study aims to provide a large-scale analysis of public discourse on family violence and COVID-19 on Twitter using machine learning techniques to fill this gap

  • We found that the rising rate of domestic violence was associated with risk factors: “drug abuse,” “alcohol abuse,” “financial constraints”, “guns,” “trafficking,” and “COVID-19 related”

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Summary

Introduction

As seen in the case of Ebola, epidemics increase the rates of domestic violence [1]. The World Health Organization declaredCOVID-19 a pandemic on March 11, 2020. 1 (page number not for citation purposes) business, travel restrictions, and quarantine Even though these measures are useful for infection control [2], they bring a series of negative social consequences, such as psychological stress [3,4,5], unemployment [6], ageism [7], and increased rates of violence against women and children [8,9,10,11]. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time

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