Detecting Deceptive Opinions with Profile Compatibility
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
We propose using profile compatibility to differentiate genuine and fake product re-views. For each product, a collective profile is derived from a separate col-lection of reviews. Such a profile con-tains a number of aspects of the prod-uct, together with their descriptions. For a given unseen review about the same product, we build a test profile using the same approach. We then perform a bidi-rectional alignment between the test and the collective profile, to compute a list of aspect-wise compatible features. We adopt Ott et al. (2011)’s op spam v1.3 dataset for identifying truthful vs. decep-tive reviews. We extend the recently pro-posed N-GRAM+SYN model of Feng et al. (2012a) by incorporating profile compat-ibility features, showing such an addition significantly improves upon their state-of-art classification performance. 1
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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