Employing Media Messaging Strategies to Respond to COVID-19 Misinformation
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has revealed critical gaps in the public’s knowledge of infectious diseases. Experts, including the World Health Organization, acknowledge that an “infodemic” of misinformation is spreading at the same time as the pandemic. Furthermore, 13% of Canadians age 50 and younger reported using social media as their primary source of information about COVID-19. Thus, in January 2020, the Infectious Disease Working Group (IDWG) was formed by a group of students at the Dalla Lana School of Public Health, University of Toronto. The IDWG’s Media Messaging Team (MMT) uses Knowledge Translation (KT) strategies to increase access to evidence-based information related to public health and COVID-19. Specifically, MMT uses virtual platforms, including Twitter and Instagram (@infectious_info), to disseminate information to a wide audience. Objectives: The MMT aims to produce content to dispel pervasive and harmful myths about COVID-19, raise public awareness, and advocate for health equity. Methods: The team creates 2-4 pieces of original content per week on topics such as Ontario Government legislation updates, myth-busting series, and “Wednesday Series” (summaries of novel research findings). The IDWG employs an equity lens to ensure that the content takes into account the experiences and needs of diverse groups, and that graphics are representative of a diverse audience. Health communication strategies are used to promote audience engagement through compelling and bold content design. Results: The Instagram account has over 4,400 followers, with some posts surpassing 50,000 views. Qualitative feedback from social media followers indicates that this project is addressing an emerging gap in knowledge resulting from unclear messaging from official bodies, the spread of mis/disinformation, and disparities in health literacy levels. Conclusions: The findings can inform the development and implementation of KT strategies to reach a wide audience and increase the uptake of public health information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".