Disinformation and democratic threats: Insights from the 2019 Canadian federal election
Bibliographic record
Abstract
News and social media shape voter decisions by influencing which political issues receive attention and how they are presented. This study examines how exposure to social media and disinformation impacted voter behaviour during the 2019 Canadian federal election. A survey was designed and delivered via various social media channels to collect data from Canadians who voted in the 2019 election (N = 182). Participants were presented with a mix of real and fake news headlines, and their responses were analyzed using binary logistic regression to assess the impact of media exposure on voting decisions. The results highlight that time spent on social media, particularly Instagram, significantly increased the likelihood of participants changing their voting decisions. Even when not widely circulated, exposure to fake news profoundly influenced voting decisions among respondents. Interestingly, real news headlines showed no statistically significant effect on voting behaviour, suggesting a reduced impact of credible journalism compared to other media types. This study emphasizes the necessity to create well-informed strategies to mitigate the spread of fake news and enhance media literacy to safeguard democratic processes in the digital age. This research contributes to theoretical advancements in understanding disinformation’s impacts and provides relevant insights for policymakers, educators, and media platforms working to mitigate the influence of disinformation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".