Engagement with candidate posts on Twitter, Instagram, and Facebook during the 2019 election
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
Social media are critical tools offering connections between political actors, voters, and journalists. However, existing scholarship rarely assesses how user engagement differs by platform, content, and function of the post. We examine Facebook ( n = 938), Instagram ( n = 258), and Twitter ( n = 1771) posts by the leaders of three major political parties in Canada during the 2019 Federal Election. Across all three platforms, Liberal Leader Trudeau’s posts receive the most engagement. On Twitter, attack posts receive slightly more engagement and interaction posts receive less engagement, compared with other platforms. While policy posts produce lower levels of engagement across platforms, Facebook is distinctive in yielding the lowest levels of user engagement on policy posts. In sum, our findings suggest that political leaders should tailor the content of their social media posts to different platforms.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it