She Said, She Said: Differential Interpersonal Similarities Predict Unique Linguistic Mimicry in Online Word of Mouth
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.
Bibliographic record
Abstract
This research examines the antecedents, causes, and consequences of linguistic mimicry, which assesses how closely individuals match others’ word use. We examine mimicry of linguistic style (how things are said) and content (what is said) in online word of mouth (WOM). To our knowledge, this research provides the first demonstration of unique linguistic mimicry, where consumers engaging in online WOM differentially mimic other posters’ word use. Two experiments and one study using field data show that when consumers are personally similar to an individual who has previously posted (e.g., same gender), they mimic this individual’s positive emotion and social word use. When consumers are similar in status to an individual who has previously posted (e.g., same forum ranking), they mimic this individual’s cognitive and descriptive word use. This differential mimicry is driven by affiliation versus achievement goals, respectively, and affects consumers’ engagement in online WOM in terms of posting incidence and volume.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it