The Medium may be the Same but the Message is Different: Comparing the Tweets of U.S. Presidents Obama and Trump
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
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.
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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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