The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets
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Abstract
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.
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SciScore for 10.1101/2020.08.13.20167452: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Data collection and sample: The sampling frame was our COVID-19 dataset between April 12 to July 16, 2020, which used a list of COVID-19 relevant hashtags as search terms to randomly collect Tweets from Twitter (Xue et al., 2020a, b). Blinding not detected. Power Analysis not detected. Sex as a biological variable We sampled Tweets using keywords, including “domestic violence,” “intimate partner violence,” “family violence,” “violence against women,” “gender-based violence,” “child abuse,” “child maltreatment,” “elder abuse” and “IPV. Table 2: Resources
Software and Algorithms Sentences Resources We used Python to clean the data and removed the … SciScore for 10.1101/2020.08.13.20167452: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Data collection and sample: The sampling frame was our COVID-19 dataset between April 12 to July 16, 2020, which used a list of COVID-19 relevant hashtags as search terms to randomly collect Tweets from Twitter (Xue et al., 2020a, b). Blinding not detected. Power Analysis not detected. Sex as a biological variable We sampled Tweets using keywords, including “domestic violence,” “intimate partner violence,” “family violence,” “violence against women,” “gender-based violence,” “child abuse,” “child maltreatment,” “elder abuse” and “IPV. Table 2: Resources
Software and Algorithms Sentences Resources We used Python to clean the data and removed the following items because they had no contribution to the semantic meanings of the Tweets, including the hashtag symbol, URLs, @users, special characters, punctuations, and stop-words. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The present study overcomes the limitation of existing scholarship that lacks data or anecdotal reports. We contribute to the understanding of family violence during the pandemic by providing surveillance in Tweets, which is essential to identify potentially effective policy programs in offering targeted support for victims and survivors and preparing for the next wave. Victims of family violence and the COVID-19 pandemic: Our results provide insights about who are at higher risks of family violence during the lockdown. Findings reveal a broader range of family violence regardless of gender, such as the LGBTQ community (e.g., men men). To compare our results with one recent study using Twitter data for domestic violence research (Xue et al., 2019a), we also find that domestic violence-related discussions focus on the support and protection of victims instead of interventions against abusers. Women and children are disproportionately affected by family violence that is consistent with the majority of the research in the field (Bradbury-Jones & Isham, 2020; Fantuzzo et al., 1997; Tjaden & Thoennes, 1998; Xue, Chen, & Gelles, 2019a). Violence against children is associated with the characteristics of a disaster in previous epidemic studies (Roje Ðapić et al., 2020). UNICEF (2020) reports that school closures contribute to the increasing rates of child (sexual) abuse and neglect during the Ebola epidemic. It is also important to note that child abuse and domestic violence are lik...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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