Extremists on the Left and Right Use Angry, Negative Language
Bibliographic record
Abstract
We propose that political extremists use more negative language than moderates. Previous research found that conservatives report feeling happier than liberals and yet liberals “display greater happiness” in their language than do conservatives. However, some of the previous studies relied on questionable measures of political orientation and affective language, and no studies have examined whether political orientation and affective language are nonlinearly related. Revisiting the same contexts (Twitter, U.S. Congress), and adding three new ones (political organizations, news media, crowdsourced Americans), we found that the language of liberal and conservative extremists was more negative and angry in its emotional tone than that of moderates. Contrary to previous research, we found that liberal extremists’ language was more negative than that of conservative extremists. Additional analyses supported the explanation that extremists feel threatened by the activities of political rivals, and their angry, negative language represents efforts to communicate as much to others.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".