How Language Shapes Word of Mouth's Impact
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
Word of mouth affects consumer behavior, but how does the language used in word of mouth shape that impact? Might certain types of consumers be more likely to use certain types of language, affecting whose words have more influence? Five studies, including textual analysis of more than 1,000 online reviews, demonstrate that compared to more implicit endorsements (e.g., “I liked it,” “I enjoyed it”), explicit endorsements (e.g., “I recommend it”) are more persuasive and increase purchase intent. This occurs because explicit endorsers are perceived to like the product more and have more expertise. Looking at the endorsement language consumers actually use, however, shows that while consumer knowledge does affect endorsement style, its effect actually works in the opposite direction. Because novices are less aware that others have heterogeneous product preferences, they are more likely to use explicit endorsements. Consequently, the endorsement styles novices and experts tend to use may lead to greater persuasion by novices. These findings highlight the important role that language, and endorsement styles in particular, plays in shaping the effects of word of mouth.
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 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 | 0.044 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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