Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data

Characterizing the Gendered Twitter Discussion of COVID-19 Hoax

We collected data from Twitter and used content analysis to better understand the gendered discussion around COVID-19 as a hoax. We identified three main categories in the inductive stage of the research: (1) sympathetic to human rights & perceived injustice, (2) invincibility and superiority of COVID hoaxers, (3) conspiracies and/or hidden agendas. The findings of the study show that among all gender groups, the first category is the most dominant (44.4%), the third category is the second most frequent (35.6%), and the last category (19.9%) is the least frequent. However, when the discussion is centered on men (40.2%) and gender and sexual minorities (GSM; 69.6%) groups, the last category is the most dominant with regard to stigmatizing GSM groups by falsely associating them with progressive secret agendas. As for women's group, being sympathetic to human rights and the perceived injustice against them during the pandemic constitute the most dominant category (51.5%). We discuss the implications of the study in the conclusion.

Twitter Misinformation Discourses About Vaping: Systematic Content Analysis

BACKGROUND

While there has been substantial analysis of social media content deemed to spread misinformation about electronic nicotine delivery systems use, the strategic use of misinformation accusations to undermine opposing views has received limited attention.

OBJECTIVE

This study aims to fill this gap by analyzing how social media users discuss the topic of misinformation related to electronic nicotine delivery systems, notably vaping products. Additionally, this study identifies and analyzes the actors commonly blamed for spreading such misinformation and how these claims support both the provaping and antivaping narratives.

METHODS

Using Twitter's (subsequently rebranded as X) academic application programming interface, we collected tweets referencing #vape and #vaping and keywords associated with fake news and misinformation. This study uses systematic content analysis to analyze the tweets and identify common themes and actors who discuss or possibly spread misinformation.

RESULTS

This study found that provape users dominate the platform regarding discussions about misinformation about vaping, with provaping tweets being more frequent and having higher overall user engagement. The most common narrative for provape tweets surrounds the conversation of vaping being perceived as safe. On the other hand, the most common topic from the antivape narrative is that vaping is indeed harmful. This study also points to a general distrust in authority figures, with news outlets, public health authorities, and political actors regularly accused of spreading misinformation, with both placing blame. However, specific actors differ depending on their positionalities. The vast number of accusations from provaping advocates is found to shape what is considered misinformation and works to silence other narratives. Additionally, allegations against reliable and proven sources, such as public health authorities, work to discredit assessments about the health impacts, which is detrimental to public health overall for both provaping and antivaping advocates.

CONCLUSIONS

We conclude that the spread of misinformation and the accusations of misinformation dissemination using terms such as "fact check," "misinformation," "fake news," and "disinformation" have become weaponized and co-opted by provaping actors to delegitimize criticisms about vaping and to increase confusion about the potential health risks. The study discusses the mixed types of impact of vaping on public health for both smokers and nonsmokers. Additionally, we discuss the implications for effective health education and communication about vaping and how misinformation claims can affect evidence-based discourse on Twitter as well as informed vaping decisions.

The hybrid political role of feminism on Twitter during COVID-19: SISMA Mujer in Colombia

Twitter proved to be strategic for the dissemination of information, and for the activation of feminist social movements. This article identifies the patterns of representation around feminist movements on Twitter during the COVID-19 pandemic. We analyzed the discourse around a Colombian NGO known as Sisma Mujer, in a corpus of 4415 tweets posted during the first year of COVID-19. The results showed five significant topic categories: gender-based violence, women in peacebuilding, women's human rights, gender equality, and social protest. This activity re-contextualized the online activism of this movement into a new, hybrid role with important political implications for the social movement. Our analysis highlights this role by pointing out how feminist activists framed gender-based violence to generate a discourse on Twitter.

Stigmatization in the context of the COVID-19 pandemic: a survey experiment using attribution theory and the familiarity hypothesis

BACKGROUND

The COVID-19 pandemic has created a global health crisis, leading to stigmatization and discriminatory behaviors against people who have contracted or are suspected of having contracted the virus. Yet the causes of stigmatization in the context of COVID-19 remain only partially understood. Using attribution theory, we examine to what extent attributes of a fictitious person affect the formation of stigmatizing attitudes towards this person, and whether suspected COVID-19 infection (vs. flu) intensifies such attitudes. We also use the familiarity hypothesis to explore whether familiarity with COVID-19 reduces stigma and whether it moderates the effect of a COVID-19 infection on stigmatization.

METHODS

We conducted a multifactorial vignette survey experiment (2⁸-design, i.e., N_Vignettes = 256) in Germany (N_Respondents = 4,059) in which we experimentally varied signals and signaling events (i.e., information that may trigger stigma) concerning a fictitious person in the context of COVID-19. We assessed respondents' cognitive (e.g., blameworthiness) and affective (e.g., anger) responses as well as their discriminatory inclinations (e.g., avoidance) towards the character. Furthermore, we measured different indicators of respondents' familiarity with COVID-19.

RESULTS

Results revealed higher levels of stigma towards people who were diagnosed with COVID-19 versus a regular flu. In addition, stigma was higher towards those who were considered responsible for their infection due to irresponsible behavior. Knowing someone who died from a COVID infection increased stigma. While higher self-reported knowledge about COVID-19 was associated with more stigma, higher factual knowledge was associated with less.

CONCLUSION

Attribution theory and to a lesser extent the familiarity hypothesis can help better understand stigma in the context of COVID-19. This study provides insights about who is at risk of stigmatization and stigmatizing others in this context. It thereby allows identifying the groups that require more support in accessing healthcare services and suggests that basic, factually oriented public health interventions would be promising for reducing stigma.

#WuhanDiary and #WuhanLockdown: gendered posting patterns and behaviours on Weibo during the COVID-19 pandemic

Social media can be both a source of information and misinformation during health emergencies. During the COVID-19 pandemic, social media became a ubiquitous tool for people to communicate and represents a rich source of data researchers can use to analyse users’ experiences, knowledge and sentiments. Research on social media posts during COVID-19 has identified, to date, the perpetuity of traditional gendered norms and experiences. Yet these studies are mostly based on Western social media platforms. Little is known about gendered experiences of lockdown communicated on non-Western social media platforms. Using data from Weibo, China’s leading social media platform, we examine gendered user patterns and sentiment during the first wave of the pandemic between 1 January 2020 and 1 July 2020. We find that Weibo posts by self-identified women and men conformed with some gendered norms identified on other social media platforms during the COVID-19 pandemic (posting patterns and keyword usage) but not all (sentiment). This insight may be important for targeted public health messaging on social media during future health emergencies.

Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news of “E-learning”, which is considered a big challenge, as online textual data related to the education sector is considered of great importance. On the other hand, fake news and misinformation related to COVID-19 have confused parents, students, and teachers. An efficient detection approach should be used to gather more precise information in order to identify COVID-19 disinformation. Tweet records (people’s opinions) have gained significant attention worldwide for understanding the behaviors of people’s attitudes. SA of the COVID-19 education sector still does not provide a clear picture of the information available in these tweets, especially if this misinformation and fake news affect the field of E-learning. This study has proposed denoising AutoEncoder to eliminate noise in information, the attentional mechanism for a fusion of features as parts where a fusion of multi-level features and ELM-AE with LSTM is applied for the task of SA classification. Experiments show that our suggested approach obtains a higher F1-score value of 0.945, compared with different state-of-the-art approaches, with various sizes of testing and training datasets. Based on our knowledge, the proposed model can learn from unified features set to obtain good performance, better results than one that can be learned from the subset of features.

#DomesticViolence During the COVID-19 Global Pandemic: An Analysis of Public Commentary via Twitter

The current study sought to characterize commentary regarding intimate partner violence during the COVID-19 (SARS-CoV-2) pandemic via the Twitter hashtags #DomesticAbuse and #DomesticViolence. A sample of 481 original, English-language tweets containing the hashtag #DomesticAbuse or #DomesticViolence posted across five consecutive weekdays from March 22 to March 27, 2020-during which many places were enacting lockdown mandates-was examined using thematic content analyses. Overall, Twitter users commented on potential increased rates of IPV, while adding details about abuse tactics that could be employed by perpetrators during the pandemic. Additionally, Twitter users disclosed personal experiences of IPV victimization. Four themes were identified, including (1) type of domestic violence (i.e. whether the violence was COVID-specific or general domestic violence), (2) commentary about IPV (i.e. general reflections, decentralizing and centralizing survivorhood), (3) perpetrator tactic (i.e. abuse tactic used by the perpetrator), and (4) institutions responsible (i.e. institutions responsible for providing services to survivors). Overall, the commentary on Twitter reflected an effort to raise awareness and share informational aid for potential victims/survivors of IPV. Data highlight the potential of social media networks in conveniently facilitating the sharing and spreading of useful resources to other users. Future research should examine whether resources shared via Twitter reach individuals who need them and empower individuals to garner support.

Advances of Sentiment Analysis Applications in Obstetrics/Gynecology and Midwifery

BACKGROUND

Sentiment analysis, which is also referred to as 'opinion mining' or 'emotion AI', processes natural language, analyzes text and employs computational linguistics, and biometrics to identify and analyze emotions and subjective information. Sentiment analysis is mostly applied in domains such as marketing and customer service but also in clinical medicine. Clinical medicine- related sentiment analysis has advanced recently, as more and more researchers are performing studies with the help of this valuable technique, having noticed its ability to contribute in the field.

OBJECTIVE

The aim of this review was to present important facts about sentimental analysis described in deposited articles in on-line databases and the relevant articles critically appraised and a narrative synthesis conducted.

METHODS

A systematic search of four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect) was performed. This review considered only quantitative, primary studies in English language, without geographical limitations, published from 2006-2021 and relevant to the objective. Searching terms were 'Sentiment analysis' AND 'Obstetrics' OR 'pregnancy', OR 'COVID' OR 'Perinatal distress' OR 'postpartum period' OR 'fetal' OR 'breast feeding' OR 'cervical'.

RESULTS AND DISCUSSION

Relevant articles were critically appraised and a narrative synthesis was conducted. As a large number of studies, illustrates the use of sentiment analysis in the domain of clinical medicine, it is proved to be extremely helpful, assisting in the investigation of some highly important and even previously unexplored issues.

CONCLUSION

Since pregnant women express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment. Given the vast knowledge sentiment analysis has already offered, further studies employing this technique are expected in the future.