National character stereotypes mirror language use: A study of Canadian and American tweets
Bibliographic record
Abstract
National character stereotypes, or beliefs about the personality characteristics of the members of a nation, present a paradox. Such stereotypes have been argued to not be grounded in the actual personality traits of members of nations, yet they are also prolific and reliable. Stereotypes of Canadians and Americans exemplify the paradox; people in both nations strongly believe that the personality profiles of typical Canadians and Americans diverge, yet aggregated self-reports of personality profiles of Canadians and Americans show no reliable differences. We present evidence that the linguistic behavior of nations mirrors national character stereotypes. Utilizing 40 million tweets from the microblogging platform Twitter, in Study 1A we quantify the words and emojis diagnostic of Canadians and Americans. In Study 1B we explore the positivity of national language use. In Studies 2A and 2B, we present the 120 most nationally diagnostic words and emojis of each nation to naive participants, and ask them to assess personality of a hypothetical person who uses either diagnostically Canadian or American words and emojis. Personality profiles derived from the diagnostic words of each nation bear close resemblance to national character stereotypes. We therefore propose that national character stereotypes may be partially grounded in the collective linguistic behaviour of nations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".