Effects of Pre-trained Word Embeddings on Text-based Deception Detection
Bibliographic record
Abstract
With e-commerce transforming the way in which individuals and businesses conduct trades, online reviews have become a great source of information among consumers. With 93% of shoppers relying on online reviews to make their purchasing decisions, the credibility of reviews should be strongly considered. While detecting deceptive text has proven to be a challenge for humans to detect, it has been shown that machines can be better at distinguishing between truthful and deceptive online information by applying pattern analysis on a large amount of data. In this work, we look at the use of several popular pre-trained word embeddings (Word2Vec, GloVe, fastText) with deep neural network models (CNN, BiLSTM, CNN-BiLSTM) to determine the influence of word embedding on the accuracy of detecting deception. Some pre-trained word embeddings have shown to adversely affect the classification accuracy when compared to training the model on text embedding using the domain specific data. Through the combination of CNN and BiLSTM along with the fastText pre-trained word embeddings, we were able to achieve an accuracy of 88.8 percent on the hotel review dataset published by Ott et al. in 2011.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".