She Said, She Said: Differential Interpersonal Similarities Predict Unique Linguistic Mimicry in Online Word of Mouth
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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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".