Mining social media divides: an analysis of K-12 U.S. School uses of Twitter
Bibliographic record
Abstract
This study utilizes public data mining to explore participation divides of all available K-12 institutional Twitter accounts in the U.S. (n = 8275 accounts, n = 9,216,853 tweets). Results indicated that U.S. schools used Twitter to broadcast information on a variety of topics in a unidirectional manner and that hashtags included a variety of intended purposes, including affinity spaces, education topics, emotive language, and events. Those schools in wealthier, more populated areas were more likely to use Twitter, with wealthy, suburban schools being the most likely to use it and poor, rural schools being the least likely. Furthermore, factors such as charter school status and urbanity influenced the content of school tweets on key issues, with schools in more populated areas tweeting more about coding and college than schools in less populated areas and charter schools tweeting more about college and the politicized educational issue of common core than non-charters. These results reveal participation differences between schools based upon demographics and provides a basis for conducting future large-scale work on publicly available artifacts, such as school tweets, that may be meaningfully used as education research data.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".