Can-PolNews: A Multi-Platform Dataset of Political Discourse in Canada
Bibliographic record
Abstract
For many societies, social media has become the primary venue for encountering and engaging with political discourse. But whereas ideas and conversations span multiple communities and move fluidly between different platforms, publicly available datasets are often limited to a single platform. In this paper, we present a multi-platform social media dataset focused on political discourse in Canada, spanning January 1st, 2023, to January 1st, 2025. Our dataset contains all content posted to social media by Canadian news media (national,provincial, and local) and Canadian politicians (federal and provincial) for 1,852 unique accounts across four major platforms popular among Canadians: Instagram, X/Twitter, TikTok, and YouTube. Politicians are labeled by their political party affiliations and provinces, facilitating comparative analysis of regional political trends and ideological affinities. By covering a two-year time frame, this dataset, containing more than 5 million posts with a normalized schema across four platforms, enables researchers to analyze patterns and trends of digital political engagement, and the interplay of news and political elites on social media in an established democracy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".