From high hopes to disenchantment: A qualitative analysis of editorial cartoons on COVID-19 vaccines in Canadian newspapers.

In Canada, the first COVID-19 vaccine was approved for use in December 2020, marking the beginning of a large vaccination campaign. The campaign was not only unprecedented in terms of reach, but also with regards to the amount of information about vaccines that circulated in traditional and social media. This study's aim was to describe COVID-19 vaccine related discourses in Canada through an analysis of editorial cartoons. We collected 2172 cartoons about COVID-19 published between January 2020 and August 2022 in Canadian newspapers. These cartoons were downloaded and a first thematic analysis was conducted using the WHO-EPIWIN taxonomy (cause, illness, treatment, interventions, and information). From this, 389 cartoons related to COVID-19 vaccines were identified under the treatment category. These were subjected to a second thematic analysis to assess main themes (e.g., vaccine development, campaign progress, etc.), characters featured (e.g., politicians, public figures, public) and position with respect to vaccine (favorable, unfavorable, neutral). Six main themes emerged: Research and development of vaccines; Management of the vaccination campaign; Perceptions of and experiences with vaccination services; Measures and incentives to increase COVID-19 vaccine uptake; Criticism of the unvaccinated; and Effectiveness of vaccination. Our analysis revealed a shift in attitudes toward COVID-19 vaccination from high hopes to disenchantment, which may reflect some vaccine fatigue. In the future, public health authorities could face some challenges in maintaining confidence and high COVID-19 vaccine uptake.

Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster-Based BERT Topic Modeling Approach.

Background: The COVID-19 pandemic has spotlighted the politicization of public health issues. A public health monitoring tool must be equipped to reveal a public health measure's political context and guide better interventions. In its current form, infoveillance tends to neglect identity and interest-based users, hence being limited in exposing how public health discourse varies by different political groups. Adopting an algorithmic tool to classify users and their short social media texts might remedy that limitation. Objective: We aimed to implement a new computational framework to investigate discourses and temporal changes in topics unique to different user clusters. The framework was developed to contextualize how web-based public health discourse varies by identity and interest-based user clusters. We used masks and mask wearing during the early stage of the COVID-19 pandemic in the English-speaking world as a case study to illustrate the application of the framework. Methods: We first clustered Twitter users based on their identities and interests as expressed through Twitter bio pages. Exploratory text network analysis reveals salient political, social, and professional identities of various user clusters. It then uses BERT Topic modeling to identify topics by the user clusters. It reveals how web-based discourse has shifted over time and varied by 4 user clusters: conservative, progressive, general public, and public health professionals. Results: This study demonstrated the importance of a priori user classification and longitudinal topical trends in understanding the political context of web-based public health discourse. The framework reveals that the political groups and the general public focused on the science of mask wearing and the partisan politics of mask policies. A populist discourse that pits citizens against elites and institutions was identified in some tweets. Politicians (such as Donald Trump) and geopolitical tensions with China were found to drive the discourse. It also shows limited participation of public health professionals compared with other users. Conclusions: We conclude by discussing the importance of a priori user classification in analyzing web-based discourse and illustrating the fit of BERT Topic modeling in identifying contextualized topics in short social media texts.

Stigma and blame related to COVID-19 pandemic: A case-study of editorial cartoons in Canada.

The COVID-19 pandemic represents not only the spread of a highly contagious and potentially fatal virus, but also an outbreak of theories, rumors, discourses and representations trying to make sense of a crisis. In this article, we explore the issue of blame and stigma in the context of the COVID-19 pandemic in Canada. We do so by studying editorial cartoons published about COVID-19 in ten mainstream Canadian newspapers between January 2020 and March 2021. We identified 203 editorial cartoons that highlight common discourses which blame or stigmatize specific groups of people for the origin or transmission of COVID-19, or for their behavior during the pandemic. The cartoons focused on four groups: 1) people of Chinese origin or descent and of other national/geographic provenance (Americans, Canadians from specific provinces, urban residents); 2) international travelers; 3) people who do not respect the preventive measures to contain the pandemic; and 4) people who question or criticize the scientific discourses about COVID-19. Our analysis revealed an "othering process" common in times of pandemic. Our analysis of editorial cartoons in Canada also uncovered a moralization around the respect of the counter measures against COVID-19. These editorial cartoons largely divide the population into two groups: 1) "virtuous" people who are "selfless" and "smart" and who respect the public health preventive measures; 2) those who are "immoral", "self-centered", "silly" and even "stupid", who do not respect the recommended measures to prevent the transmission of COVID-19. While negatively portraying these individuals may help promote adherence to the recommended measures, it also can exacerbate polarization. Analyzing editorial cartoons can be a useful approach to rapidly gather information on attitudes and feelings in the public at a specific time and place.

Developing a multi-level organization-public dialogic communication framework to assess social media-mediated disaster communication and engagement outcomes.

Dialogic communication has long been viewed as vital for effective organization-public relations. Yet, it is under-theorized whether and how organizations' disaster communication messages may embody dialogic communication principles, and how various dialogic features are associated with different public engagement outcomes on social media. Extending the Organization-Public Dialogic Communication (OPDC) framework to the context of social media-mediated disaster communication, we propose a multi-level framework to assess the dialogic capacity of Facebook messages sent by disaster management organizations during a natural disaster. Three levels of dialogic communication characteristics (i.e., message structure-level, topic-level, and linguistic level) are examined using content analysis and Linguistic Inquiry and Word Count (LIWC). Results identified media richness, correcting, and confirming topics as three consistent predictors of public engagement of all types. Meanwhile, there exhibit greater variations regarding how other topical features and linguistic characteristics are related to public's cognitive, emotional, and behavioral engagement during a disaster.