Public Sentiment and Discourse on Domestic Violence During the COVID-19 Pandemic in Australia: Analysis of Social Media Posts

Public Attitudes Toward Violence Against Doctors: Sentiment Analysis of Chinese Users

BACKGROUND

Violence against doctors attracts the public's attention both online and in the real world. Understanding how public sentiment evolves during such crises is essential for developing strategies to manage emotions and rebuild trust.

OBJECTIVE

This study aims to quantify the difference in public sentiment based on the public opinion life cycle theory and describe how public sentiment evolved during a high-profile crisis involving violence against doctors in China.

METHODS

This study used the term frequency-inverse document frequency (TF-IDF) algorithm to extract key terms and create keyword clouds from textual comments. The latent Dirichlet allocation (LDA) topic model was used to analyze the thematic trends and shifts within public sentiment. The integrated Chinese Sentiment Lexicon was used to analyze sentiment trajectories in the collected data.

RESULTS

A total of 12,775 valid comments were collected on Sina Weibo about public opinion related to a doctor-patient conflict. Thematic and sentiment analyses showed that the public's sentiments were highly negative during the outbreak period (disgust: 10,201/30,433, 33.52%; anger: 6792/30,433, 22.32%) then smoothly changed to positive and negative during the spread period (sorrow: 2952/8569, 34.45%; joy: 2782/8569, 32.47%) and tended to be rational and peaceful during the decline period (joy: 4757/14,543, 32.71%; sorrow: 4070/14,543, 27.99%). However, no matter how emotions changed, each period's leading tone contained many negative sentiments.

CONCLUSIONS

This study simultaneously examined the dynamics of theme change and sentiment evolution in crises involving violence against doctors. It discovered that public sentiment evolved alongside thematic changes, with the dominant negative tone from the initial stage persisting throughout. This finding, distinguished from prior research, underscores the lasting influence of early public sentiment. The results offer valuable insights for medical institutions and authorities, suggesting the need for tailored risk communication strategies responsive to the evolving themes and sentiments at different stages of a crisis.

An Exploration of Australian Online Government Portals for Women Experiencing Domestic Violence During the COVID-19 Pandemic

Domestic violence against women increased during COVID-19 lockdowns. This inaugural study examined the content of Australian government online portals, for women seeking support and help for domestic violence, during the 2021 COVID-19 pandemic. This mixed methods study incorporated four phases: a search; measurement of portal quality standard using DISCERN; enumeration of portal items; and a qualitative exploration of portal text. Australian governments must continue to work alongside domestic violence services as we found some portals were better than others. Continued review, revision, and funding are needed to meet the demands associated with this evolving public health emergency.

Gender-Based Violence Narratives in Internet-Based Conversations in Nigeria: Social Listening Study

BACKGROUND

Overcoming gender inequities is a global priority recognized as essential for improved health and human development. Gender-based violence (GBV) is an extreme manifestation of gender inequities enacted in real-world and internet-based environments. In Nigeria, GBV has come to the forefront of attention since 2020, when a state of emergency was declared due to increased reporting of sexual violence. Understanding GBV-related social narratives is important to design public health interventions.

OBJECTIVE

We explore how gender-related internet-based conversations in Nigeria specifically related to sexual consent (actively agreeing to sexual behavior), lack of consent, and slut-shaming (stigmatization in the form of insults based on actual or perceived sexuality and behaviors) manifest themselves and whether they changed between 2017 and 2022. Additionally, we explore what role events or social movements have in shaping gender-related narratives in Nigeria.

METHODS

Social listening was carried out on 12,031 social media posts (Twitter, Facebook, forums, and blogs) and almost 2 million public searches (Google and Yahoo search engines) between April 2017 and May 2022. The data were analyzed using natural language processing to determine the most salient conversation thematic clusters, qualitatively analyze time trends in discourse, and compare data against selected key events.

RESULTS

Between 2017 and 2022, internet-based conversation about sexual consent increased 72,633%, from an average 3 to 2182 posts per month, while slut-shaming conversation (perpetrating or condemning) shrunk by 9%, from an average 3560 to 3253 posts per month. Thematic analysis shows conversation revolves around the objectification of women, poor comprehension of elements of sexual consent, and advocacy for public education about sexual consent. Additionally, posters created space for sexual empowerment and expressions of sex positivity, pushing back against others who weaponize posts in support of slut-shaming narrative. Time trend analysis shows a greater sense of empowerment in advocating for education around the legal age of consent for sexual activity, calling out double standards, and rejecting slut-shaming. However, analysis of emotions in social media posts shows anger was most prominent in sexual consent (n=1213, 73%) and slut-shaming (n=226, 64%) posts. Organic social movements and key events (#ArewaMeToo and #ChurchToo, the #SexforGrades scandal, and the #BBNaija television program) played a notable role in sparking discourse related to sexual consent and slut-shaming.

CONCLUSIONS

Social media narratives are significantly impacted by popular culture events, mass media programs, social movements, and micro influencers speaking out against GBV. Hashtags, media clips, and other content can be leveraged effectively to spread awareness and spark conversation around evolving gender norms. Public health practitioners and other stakeholders including policymakers, researchers, and social advocates should be prepared to capitalize on social media events and discourse to help shape the conversation in support of a normative environment that rejects GBV in all its forms.

Using Twitter-Based Data for Sexual Violence Research: Scoping Review

BACKGROUND

Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study.

OBJECTIVE

We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies.

METHODS

We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles.

RESULTS

We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations.

CONCLUSIONS

We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research.

Injury mortality and morbidity changes due to the COVID-19 pandemic in the United States

Introduction

The COVID-19 pandemic significantly changed society. We aimed to examine the systematic impact of the COVID-19 on injury burden in the United States.

Methods

We extracted mortality and morbidity data from CDC WONDER and WISQARS. We estimated age-standardized injury mortality rate ratio and morbidity rate ratio (MtRR and MbRR) with 95% confidence interval (95% CI) for all injuries, all unintentional injuries, homicide/assault by all methods, suicide/self-harm by all methods, as well as other 11 specific unintentional or intentional injury categories. Injury rate ratios were compared for 2020 vs. 2019 to those of 2019 vs. 2018 to demonstrate the influence of the COVID-19 pandemic on fatal and nonfatal injury burden. The ratio of MtRRs (RMtRR) and the ratio of MbRRs (RMbRR) with 95% CI between 2020 vs. 2019 and 2019 vs. 2018 were calculated separately.

Results

The COVID-19 pandemic was associated with an increase in injury mortality (RMtRR = 1.12, 95% CI: 1.11, 1.13) but injury morbidity decreased (RMbRR = 0.88, 95% CI: 0.88, 0.89) when the changes of these rates from 2019 to 2020 were compared to those from 2018 to 2019. Mortality disparities between the two time periods were primarily driven by greater mortality during the COVID-influenced 2020 vs. 2019 from road traffic crashes (particularly motorcyclist mortality), drug poisoning, and homicide by firearm. Similar patterns were not present from 2019 vs. 2018. There were morbidity reductions from road traffic crashes (particularly occupant and pedestrian morbidity from motor vehicle crashes), unintentional falls, and self-harm by suffocation from 2019 to 2020 compared to the previous period. Change patterns in sexes and age groups were generally similar, but exceptions were observed for some injury types.

Conclusions

The COVID-19 pandemic significantly changed specific injury burden in the United States. Some discrepancies also existed across sex and age groups, meriting attention of injury researchers and policymakers to tailor injury prevention strategies to particular populations and the environmental contexts citizens face.

Public Health Directives in a Pandemic: Paradoxical Messages for Domestic Abuse Victims in Four Countries

When the COVID-19 pandemic manifested urgent concerns were raised around the globe about the increased risk that public health restrictions could pose for victims of domestic abuse. Governments, NGOs and community services swiftly responded to convey the message that services for victims were operational and restrictions did not apply to those fleeing harm. This paper reports on the various approaches used to communicate this public health messaging during COVID-19, further highlighting strengths and learning which could inform future crises messaging. It utilises data gathered through a rapid review and mapping of policy and practice initiatives across 4 high-middle income countries: UK, Australia, South Africa and Ireland. Four themes were identified: (1) Top-down: National media messaging; (2) Top-down: Political leadership; (3) Traditional media vs. social media and (4) Bottom-up messaging: Localised, community-based messaging. It was found that a strong, clear top-down stance on domestic abuse was perceived as beneficial during COVID-19. However, a stronger focus on evaluation, reach and impact, particularly for minority groups may be required. Newer forms of media were shown to have potential in conveying messaging to minority groups. Community and grassroots organizations demonstrated their experiential knowledge in reaching target audiences. Harnessing this expertise for future crises messaging may be valuable.

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

Background

During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media.

Objective

We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media.

Methods

The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns.

Results

Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination.

Conclusions

Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.