Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviews

Abstract

Online reviews play a critical role in modern word-of-mouth communication, influencing consumers' shopping preferences and purchase decisions, and directly affecting a company's reputation and profitability. However, the credibility and authenticity of these reviews are often questioned due to the prevalence of fake online reviews that can mislead customers and harm e-commerce's credibility. These fake reviews are often difficult to identify and can lead to erroneous conclusions in user feedback analysis. This paper proposes a new approach to detect fake online reviews by combining convolutional neural network (CNN) and adaptive particle swarm optimization with natural language processing techniques. The approach uses datasets from popular online review platforms like Ott, Amazon, Yelp, TripAdvisor, and IMDb and applies feature selection techniques to select the most informative features. The paper suggests using attention mechanisms like bidirectional encoder representations from transformers and generative pre-trained transformer, as well as other techniques like Deep contextualized word representation, word2vec, GloVe, and fast Text, for feature extraction from online review datasets. The proposed method uses a multimodal approach based on a CNN architecture that combines text data to achieve a high accuracy rate of 99.4%. This outperforms traditional machine learning classifiers in terms of accuracy, recall, and F measure. The proposed approach has practical implications for consumers, manufacturers, and sellers in making informed product choices and decision-making processes, helping maintain the credibility of online consumer reviews. The proposed model shows excellent generalization abilities and outperforms conventional discrete and existing neural network benchmark models across multiple datasets. Moreover, it reduces the time complexity for both training and testing.

Deep Learning-Based Truthful and Deceptive Hotel Reviews

“…In this work, three well-known deep learning techniques-Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs)-are applied using a robust strategy, drawing on insights from the abovementioned related works. This multipronged approach seeks to attain maximum precision in tackling the issues presented by fraudulent opinion spam and phony customer evaluations [18][19][20][21][22][23][24][25][26][27][28][29].…”

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

“…A Python-based system was introduced to detect fake product reviews on Amazon, using SVM techniques to distinguish between genuine and fake reviews and enhance the reliability of product evaluations [78]. Lastly, an innovative method employing a CNN and adaptive particle swarm optimization with NLP techniques achieved a remarkable 99.4% accuracy in identifying fake online reviews, offering practical implications for consumers, manufacturers, and sellers in maintaining the trustworthiness of online reviews [79]. Another study proposed a generalized solution by fine-tuning the BERT model to predict review helpfulness, demonstrating superior performance compared to traditional bag-ofwords methods [80].…”