A helpfulness modeling framework for electronic word-of-mouth on consumer opinion platforms
Bibliographic record
Abstract
Electronic Word-of-Mouth (eWOM) is growing exponentially with the rapid development of electronic commerce. As a result, consumers are increasingly crowded by a huge amount of eWOM contents and therefore there is a need to automatically recommend eWOM contents that are helpful to them. Existing helpfulness assessment approaches that deterministically estimate the helpfulness of eWOM contents lack a generative formulation and are limited to the training set that has been voted by many readers. This article presents a rigorous probabilistic framework for inferring the “helpfulness” of eWOM contents which can build a “helpfulness” model from a low number of votes on eWOM contents. Furthermore, we introduce a measurement, “helpfulness” bias, as the benchmark for the “helpfulness” of eWOM documents. We also propose a model that exploits the graphical model and expectation maximization algorithm, under this probabilistic framework, to demonstrate the versatility of our framework. Our algorithm is compared experimentally to other existing helpfulness discovering algorithms and the experimental results show that our framework can effectively model the helpfulness of eWOM contents better than other approaches, and therefore indicate the capability of our framework to recommend helpful eWOMs to potential consumers.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".