Mostly sports and congratulations : Twitter, Facebook, and Alberta higher education
Bibliographic record
Abstract
One of the defining features of social media is the capability for interaction, specifically for the audience to respond to previously posted content. Higher education research to date has focused on the content published by institutions, with minimal examination of the content sent back to institutions. Previous research has often operated as though higher education’s social media is homogenous, without acknowledging the variation in institutional mission. Furthermore, previous research has tended to treat institutions’ social media accounts as the same, without examination of differences between social media platforms. This research examines both gaps. Twitter posts mentioning primary and secondary accounts of 25 Albertan post-secondary institutions were gathered during a two-month interval alongside messages posted to the same institutions’ primary Facebook pages, to determine which topics led the audience to communicate back to institutions and to examine whether any difference existed between account types and platforms. Analysis of the data, using sentiment analysis and topic modeling, found that audiences tended to discuss institutions’ sports teams, as well as events or features unique to institutions. No difference in sentiment or emotion was found between account types, with most messages being moderately positive. Facebook messages tended to include more descriptive language while the higher volume of tweets suggested that Twitter audiences appear to be more prone to interaction than Facebook audiences. Institutions may be best served by pursuing different social media strategies for Twitter and Facebook.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".