Node Embedding Approach for Accurate Detection of Fake Reviews: A Graph-Based Machine Learning Approach with Explainable AI

Abstract

Purpose: In recent years, online reviews have become increasingly important in promoting various products and services. Unfortunately, writing deceptive reviews has also become a common practice to promote one’s own business or tarnish the reputation of competitors. As a result, identifying fake reviews has become an intense and ongoing area of research. This paper proposes a node embedding approach to detect online fake reviews. The approach involves extracting features from the input data to create a distance matrix, which is then used to construct a Graph. From the graph, we extract graph nodes and use the Node2Vec biased random walk algorithm to create a model. We retrieve node embeddings from the Node2Vec model using Word2Vec and use different classifiers to classify the nodes. We trained and evaluated the machine learning models on the Deceptive Opinion Spam Corpus and YelpChi datasets, and achieved superior results compared to prior work for detecting fake reviews, with SVM using the ham-ming distance achieving 98.44% accuracy, 98.44% precision, 98.44% recall, and 98.44% F1-score. Furthermore, we explored different methods for explaining our proposed methods using explainable AI, demonstrating the interpretability of our approach. Our proposed node embedding approach shows promising results for 1 detecting fake reviews and offers a transparent and interpretable solution for the problem.

Graph Reduction Techniques for Instance Selection: Comparative and Empirical Study

“…Graph reduction techniques can leverage this graph structure to identify and retain the most informative nodes and edges, potentially leading to a more effective IS process compared to working with tabulated data. Graph reduction holds the potential to not only enhance computational efficiency by reducing the number of nodes but also by preserving critical relationships that are essential for maintaining the integrity of the dataset [8]. Historically, graph reduction techniques have been utilized in various domains, including social networks, computational biology, and compiler optimization.…”