Credible Review Detection with Limited Information using Consistency Features
Table 4: Kendall-Tau correlation of different models across domains.
['[BOLD] Domain', '[BOLD] Kendall-Tau-B ( [ITALIC] τb)  [BOLD] Baseline', '[BOLD] Kendall-Tau-B ( [ITALIC] τb)  [ITALIC] MYelp [BOLD]  (C-SVM)', '[BOLD] Kendall-Tau-M ( [ITALIC] τm)  [BOLD] Baseline', '[BOLD] Kendall-Tau-M ( [ITALIC] τm)  [ITALIC] MYelp [BOLD]  (C-SVM)', '[BOLD] Kendall-Tau ( [ITALIC] τb= [ITALIC] τm)  [ITALIC] MAmazon [BOLD]  (SVM-Rank)']
The baseline for the item ranking is based on the aggregated rating of all reviews on an item. The first model MYelp (C-SVM) trained on Yelp filters out the non-credible reviews, before aggregating review ratings on an item. The second model MAmazon (SVM-Rank) is trained on Amazon using SVM-Rank with the reference ranking as training labels. τb and τm for SVM-Rank are the same since there are no ties. Our first model performs substantially better than the baseline, which, in turn, is outperformed by our second model. The second model MAmazon — trained on Amazon using SVM-Rank — outperforms the former exploiting the power of the entire feature space and domain-specific proxy labels unavailable to the former.