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Record W2789443688 · doi:10.1177/0956797617744797

Persuasion, Emotion, and Language: The Intent to Persuade Transforms Language via Emotionality

2018· article· en· W2789443688 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPersuasionPsychologyEmotionalityAssociation (psychology)Social psychologyPersuasive communicationCognitive psychologyDevelopmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Persuasion is a foundational topic within psychology, in which researchers have long investigated effective versus ineffective means to change other people's minds. Yet little is known about how individuals' communications are shaped by the intent to persuade others. This research examined the possibility that people possess a learned association between emotion and persuasion that spontaneously shifts their language toward more emotional appeals, even when such appeals may be suboptimal. We used a novel quantitative linguistic approach in conjunction with controlled laboratory experiments and real-world data. This work revealed that the intent to persuade other people spontaneously increases the emotionality of individuals' appeals via the words they use. Furthermore, in a preregistered experiment, the association between emotion and persuasion appeared sufficiently strong that people persisted in the use of more emotional appeals even when such appeals might backfire. Finally, direct evidence was provided for an association in memory between persuasion and emotionality.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.041
GPT teacher head0.421
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it