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.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".