A Corpus-Based Study of Hillary Clinton’s and Donald Trump’s Linguistic Styles
Bibliographic record
Abstract
Since the 2016 U.S. presidential election, research on Hillary Clinton’s and Donald Trump’s linguistic styles has witnessed an exponential increase, with a lopsided focus on Trump in particular. This study compared Clinton’s and Trumps’ campaign speeches during the general election using a corpus-based approach. Discourse analysis of the corpora was conducted using the textual analysis software AntConc 3.2.4. The results showed that Clinton used a more diverse vocabulary compared with Trump, and that both candidates stuck to their core campaign messages in their speeches. Three major differences between Clinton’s and Trump’s linguistic styles were identified: 1) Clinton was inclined towards rational discussions of public policy, while Trump was adept at appealing to voters’ emotions; 2) Clinton was more positive and focused on her vision of the future, while Trump was more negative and fixated at depicting a dystopian reality; 3) Clinton aimed to find commonalities with the American people, while Trump aimed to highlight differences between himself and his opponents. By putting Clinton’s rhetoric on a par with Trump’s, this study highlighted their linguistic style differences as part of their grand campaign strategy, which could contribute to current understanding of the two candidates’ rhetorical preferences, political beliefs and strategies in their 2016 campaigns.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".