The Medium may be the Same but the Message is Different: Comparing the Tweets of U.S. Presidents Obama and Trump
Bibliographic record
Abstract
Monthly averages for Tweets posted by Obama in 2015-16 and Trump in 2017 were compared in terms of their frequency of occurrence, their tendency to be replies or retweets, the emotionality of their language, and their vocabulary. There were extreme differences in frequency of tweeting (r2=.88, p<.001), with Trump tweeting more frequently. There were also considerable differences in Pleasantness of Tweet language, with Obama employing more Pleasant words (r2=.31, p<.001). Trump retweeted proportionally more often while Obama replied proportionally more often (r2=.28, .20, p<.05). Additionally, each president employed a distinct vocabulary. Obama employed first person plural pronouns (“we”, “us”) more often (r2=.43, p<.001). It was possible to predict president of origin with extremely high success (97% or better) whether frequency of tweeting was included in the predictive scheme or not. While the medium the two presidents were employing was the same, their resulting messages were very different.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".