Stacking GA²M for inherently interpretable fraudulent reviewer identification by fusing target and non-target features

Abstract

This paper proposes a novel approach called Stack-GA²M to identify fraudulent reviewers in an inherently interpretable manner by fusing both target and non-target features. Specifically, for local interpretability, we adopt GA²M (Standard Generalized Additive Model plus interactions) as the basic classifier to produce three subordinate models trained by using the target features and the non-target features as review textual features and reviewer behavioral features. For global interpretability, we adopt LR (Logistic Regression) as the meta classifier to stack the outputs of three subordinate models to identify the fraudulent reviewers. The white-box model of LR enables us to understand the global interpretability of the target features and the non-target features in identifying fraudulent reviewers. With GA²M, the local interpretability of each subordinate model is derived by using feature importance, spline shape functions for individual features, and heatmaps for interaction terms. Extensive experiments on Yelp dataset demonstrate that the proposed Stack-GA²M approach is superior to state-of-the-art techniques in identifying fraudulent reviewers and exhibits favorable inherent interpretability.

Keywords:

• Fraudulent reviewer detection
• stacking model
• interpretable machine learning
• standard generalized additive model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Pandas, online: https://pandas.pydata.org/.

2 NLTK:: Natural Language Toolkit, online: https://www.nltk.org/.

3 Analyse text with Textstat, online: https://textstat.org/

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [grant nos 72174018 and 71932002]; the Beijing Natural Science Fund [grant no 9222001]; the Philosophy and Sociology Science Fund from Beijing Municipal Education Commission [grant no SZ2021110005001]