Infodemic: Social Media and the Amplification of the COVID-19 Crisis in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic will likely be remembered as profoundly transforming human relationships. Many people increased their use of social media during lockdown. Faced with the uncertainty of the situation, individuals turned to online interactions to better understand their reality. This has worsened a trend observed by researchers: the creation, spread, and reinforcement of fake news online. This misinformation not only created unfair competition with information from health authorities but also contributed to intensifying the crisis, reducing mitigation efforts, and affecting the resilience of populations (Mano, 2020). This study examines the impact of social media on exacerbating the COVID-19 crisis in Canada. Understanding this influence is crucial for evaluating the role of social media in handling health emergencies. We utilized network and content analysis techniques to illustrate that, beyond spreading fake news, an information warfare mentality drove the dissemination of disinformation during the pandemic. This dynamic significantly affected both collective and individual levels, particularly in shaping the knowledge system (a structured set of information used to detect or observe phenomena, translate them into perceived realities, and use these perceptions to make decisions) and influencing belief systems (orientations towards empirical data and other awareness) (Seitz et al., 2016). These findings suggest that the repercussions of this situation may endure within society.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it