Evolution of U.S. Presidential Discourse over 230 Years: A Psycholinguistic Perspective
Bibliographic record
Abstract
Much of recent research on U.S presidential discourse has focused on the nexus between language forms and their underlying social processes and psychological states. However, little work has been done to shed light on how these latent characteristics have evolved over time. This study investigated the evolution of three psychological states (authenticity, affect, and time orientation) underlying U.S. presidential discourse over approximately 230 years (1789–2016). Based on one of the most comprehensive corpora of presidential speech transcripts, Linguistic Inquiry and Word Count (LIWC) 2015, a text analysis software, was utilized to explore these psychological states. To see the overall trend of these states across U.S. presidential history as a whole, initial analysis was based on LIWC indices, which showed that, 1) overall, authenticity level is on a steady increase in U.S. presidential discourse; 2) in the presidents’ speeches, positive emotions invariably outweigh negative emotions, and both types of emotion remain relatively constant in the long run; 3) the discourse of “focus on present” consistently outweighs the discourse of “focus on future”, which outweighs the discourse of “focus on past”. To see whether the general trend holds across different parties, a series of independent samples t-tests were first performed to check for significant difference. The results indicated that in all the three psychological states, there was no significant difference between the Democratic presidents and the Republican presidents, and that the trend in different parties is in agreement with the overall trend. Subsequent visualization of the LIWC indices according to party generally corroborated these results, with only one exception: authenticity levels are on a steady increase in the discourse of the Democratic presidents, but in the Republican presidents, there was a sharp increase in recent years.
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.
How this classification was reachedexpand
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 | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 teacher head, 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".