Dataset Information

The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.

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.

SUBMITTER: Xue J

PROVIDER: S-EPMC7652592 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.

Xue Jia J Chen Junxiang J Chen Chen C Hu Ran R Zhu Tingshao T

Journal of medical Internet research 20201106 11

<h4>Background</h4>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.<h4>Objective</h4>This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter.<h4>Methods</h4>We analyzed over 1 million tweets r ...[more]

PMID: 33108315

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Project description:BackgroundEmerging evidence suggests that people with arthritis are reporting increased physical pain and psychological distress during the COVID-19 pandemic. At the same time, Twitter's daily usage has surged by 23% throughout the pandemic period, presenting a unique opportunity to assess the content and sentiment of tweets. Individuals with arthritis use Twitter to communicate with peers, and to receive up-to-date information from health professionals and services about novel therapies and management techniques.ObjectiveThe aim of this research was to identify proxy topics of importance for individuals with arthritis during the COVID-19 pandemic, and to explore the emotional context of tweets by people with arthritis during the early phase of the pandemic.MethodsFrom March 20 to April 20, 2020, publicly available tweets posted in English and with hashtag combinations related to arthritis and COVID-19 were extracted retrospectively from Twitter. Content analysis was used to identify common themes within tweets, and sentiment analysis was used to examine positive and negative emotions in themes to understand the COVID-19 experiences of people with arthritis.ResultsIn total, 149 tweets were analyzed. The majority of tweeters were female and were from the United States. Tweeters reported a range of arthritis conditions, including rheumatoid arthritis, systemic lupus erythematosus, and psoriatic arthritis. Seven themes were identified: health care experiences, personal stories, links to relevant blogs, discussion of arthritis-related symptoms, advice sharing, messages of positivity, and stay-at-home messaging. Sentiment analysis demonstrated marked anxiety around medication shortages, increased physical symptom burden, and strong desire for trustworthy information and emotional connection.ConclusionsTweets by people with arthritis highlight the multitude of concurrent concerns during the COVID-19 pandemic. Understanding these concerns, which include heightened physical and psychological symptoms in the context of treatment misinformation, may assist clinicians to provide person-centered care during this time of great health uncertainty.

Project description:BackgroundRecreational equipment sales rose significantly during the COVID-19 pandemic. This study investigated changes in the incidence of pediatric emergency department (PED) visits related to outdoor recreational activities during the COVID-19 pandemic.MethodsA retrospective cohort study was conducted at a large children's hospital with a level 1 trauma center. Data were obtained from PED electronic medical records of children 5-14 years with a visit from March 23-September 1 in years 2015-2020. Patients with an ICD-10 code for injury associated with recreation and use of common outdoor recreational equipment were included. Initial pandemic year, 2020, was compared with pre-pandemic years (2015-2019). Data collected included patient demographics, injury characteristics, deprivation index, and disposition. Descriptive statistics were used to characterize the population and Chi-squared analysis was used determine relationships between groups.ResultsThere were 29,044 total injury visits during the study months with 4715 visits (16.2%) due to recreational mechanisms. A higher proportion of visits due to recreational injury visits occurred during the COVID pandemic (8.2%) compared to before (4.9%). Comparing patients included within the two times, were no differences in sex, ethnicity, or ED disposition. During the COVID pandemic, there was a higher percentage of White patients (80% vs 76%) and patients with commercial insurance (64% vs 55%). There was a significantly lower deprivation index for patients injured during the COVID pandemic. There were more injuries due to bicycles, ATV/motorbike, and non-motorized wheeled vehicles during the COVID pandemic.ConclusionsDuring the COVID-19 pandemic, there was an increase in bicycle, ATV/motorbike, and non-motorized wheeled vehicle injuries. White patients with commercial insurance were more likely to be injured compared to years prior. A targeted approach to injury prevention initiatives should be considered.

Dataset Information

The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.

Publications

The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets.

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