Impressive Words: Linguistic Predictors of Public Approval of the U.S. Congress
Bibliographic record
Abstract
What type of language makes the most positive impression within a professional setting? Is competent/agentic language or warm/communal language more effective at eliciting social approval? We examined this basic social cognitive question in a real world context using a "big data" approach-the recent record-low levels of public approval of the U.S. Congress. Using Linguistic Inquiry and Word Count (LIWC), we text analyzed all 123+ million words spoken by members of the U.S. House of Representatives during floor debates between 1996 and 2014 and compared their usage of various classes of words to their public approval ratings over the same time period. We found that neither agentic nor communal language positively predicted public approval. However, this may be because communion combines two disparate social motives (belonging and helping). A follow-up analysis found that the helping form of communion positively predicted public approval, and did so more strongly than did agentic language. Next, we conducted an exploratory analysis, examining which of the 63 standard LIWC categories predict public approval. We found that the public approval of Congress was highest when politicians used tentative language, expressed both positive emotion and anxiety, and used human words, numbers, prepositions, numbers, and avoided conjunctions and the use of second-person pronouns. These results highlight the widespread primacy of warmth over competence as the primary dimensions of social cognition.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".