In the current digital commerce landscape, user-generated reviews play a critical role in shaping consumer behavior, product reputation, and platform credibility. However, the proliferation of fake or misleading reviews often generated by bots, paid agents, or AI models poses a significant threat to trust and transparency within review ecosystems. Existing detection models primarily rely on unimodal, typically textual, data and therefore fail to capture semantic inconsistencies across different modalities. To address this gap, a robust multimodal fake review detection framework is proposed, integrating textual features encoded with BERT and visual features extracted using ResNet-50. These representations are fused through a classification head to jointly predict review authenticity. To support this approach, a curated dataset comprising 21,142 user-uploaded images across food delivery, hospitality, and e-commerce domains was utilized. Experimental results indicate that the multimodal model outperforms unimodal baselines, achieving an F1-score of 0.934 on the test set. Additionally, the confusion matrix and qualitative analysis highlight the model's ability to detect subtle inconsistencies, such as exaggerated textual praise paired with unrelated or low-quality images, commonly found in deceptive content. This study demonstrates the critical role of multimodal learning in safeguarding digital trust and offers a scalable solution for content moderation across various online platforms.

With the growth of the Internet, buying habits have changed, and customers have become more dependent on the online opinions of other customers to guide their purchases. Identifying fake reviews thus became an important area for Natural Language Processing (NLP) research. However, developing high-performance NLP models depends on the availability of large amounts of training data, which are often not available for low-resource languages or domains. In this research, we used large language models to generate datasets to train fake review detectors. Our approach was used to generate fake reviews in different domains (book reviews, restaurant reviews, and hotel reviews) and different languages (English and Chinese). Our results demonstrate that our data augmentation techniques result in improved performance at fake review detection for all domains and languages. The accuracy of our fake review detection model can be improved by 0.3 percentage points on DeRev TEST, 10.9 percentage points on Amazon TEST, 8.3 percentage points on Yelp TEST and 7.2 percentage points on DianPing TEST using the augmented datasets.

Customers' reviews and feedback play crucial role on electronic commerce~(E-commerce) platforms like Amazon, Zalando, and eBay in influencing other customers' purchasing decisions. However, there is a prevailing concern that sellers often post fake or spam reviews to deceive potential customers and manipulate their opinions about a product. Over the past decade, there has been considerable interest in using machine learning (ML) and deep learning (DL) models to identify such fraudulent reviews. Unfortunately, the decisions made by complex ML and DL models - which often function as \emph{black-boxes} - can be surprising and difficult for general users to comprehend. In this paper, we propose an explainable framework for detecting fake reviews with high precision in identifying fraudulent content with explanations and investigate what information matters most for explaining particular decisions by conducting empirical user evaluation. Initially, we develop fake review detection models using DL and transformer models including XLNet and DistilBERT. We then introduce layer-wise relevance propagation (LRP) technique for generating explanations that can map the contributions of words toward the predicted class. The experimental results on two benchmark fake review detection datasets demonstrate that our predictive models achieve state-of-the-art performance and outperform several existing methods. Furthermore, the empirical user evaluation of the generated explanations concludes which important information needs to be considered in generating explanations in the context of fake review identification.

Online commerce relies heavily on user generated reviews to provide unbiased information about products that they have not physically seen. The importance of reviews has attracted multiple exploitative online behaviours and requires methods for monitoring and detecting reviews. We present a machine learning methodology for review detection and extraction, and demonstrate that it generalises for use across websites that were not contained in the training data. This method promises to drive applications for automatic detection and evaluation of reviews, regardless of their source. Furthermore, we showcase the versatility of our method by implementing and discussing three key applications for analysing reviews: Sentiment Inconsistency Analysis, which detects and filters out unreliable reviews based on inconsistencies between ratings and comments; Multi-language support, enabling the extraction and translation of reviews from various languages without relying on HTML scraping; and Fake review detection, achieved by integrating a trained NLP model to identify and distinguish between genuine and fake reviews.

Sentiment analysis, a vital component in natural language processing, plays a crucial role in understanding the underlying emotions and opinions expressed in textual data. In this paper, we propose an innovative ensemble approach for sentiment analysis for finding fake reviews that amalgamate the predictive capabilities of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers. Our ensemble architecture strategically combines these diverse models to capitalize on their strengths while mitigating inherent weaknesses, thereby achieving superior accuracy and robustness in fake review prediction. By combining all the models of our classifiers, the predictive performance is boosted and it also fosters adaptability to varied linguistic patterns and nuances present in real-world datasets. The metrics accounted for on fake reviews demonstrate the efficacy and competitiveness of the proposed ensemble method against traditional single-model approaches. Our findings underscore the potential of ensemble techniques in advancing the state-of-the-art in finding fake reviews using hybrid algorithms, with implications for various applications in different social media and e-platforms to find the best reviews and neglect the fake ones, eliminating puffery and bluffs.

Fake review identification is an important topic and has gained the interest of experts all around the world. Identifying fake reviews is challenging for researchers, and there are several primary challenges to fake review detection. We propose developing an initial research paper for investigating fake reviews by using sentiment analysis. Ten research papers are identified that show fake reviews, and they discuss currently available solutions for predicting or detecting fake reviews. They also show the distribution of fake and truthful reviews through the analysis of sentiment. We summarize and compare previous studies related to fake reviews. We highlight the most significant challenges in the sentiment evaluation process and demonstrate that there is a significant impact on sentiment scores used to identify fake feedback.

Fake reviews have become prevalent on various social networks such as e-commerce and social media platforms. As fake reviews cause a heavily negative influence on the public, timely detection and response are of great significance. To this end, effective fake review detection has become an emerging research area that attracts increasing attention from various disciplines like network science, computational social science, and data science. An important line of research in fake review detection is to utilize graph learning methods, which incorporate both the attribute features of reviews and their relationships into the detection process. To further compare these graph learning methods in this paper, we conduct a detailed survey on fake review detection. The survey presents a comprehensive taxonomy and covers advancements in three high-level categories, including fake review detection, fake reviewer detection, and fake review analysis. Different kinds of fake reviews and their corresponding examples are also summarized. Furthermore, we discuss the graph learning methods, including supervised and unsupervised learning approaches for fake review detection. Specifically, we outline the unsupervised learning approach that includes generation-based and contrast-based methods, respectively. In view of the existing problems in the current methods and data, we further discuss some challenges and open issues in this field, including the imperfect data, explainability, model efficien...

Online reviews have a significant influence on customers' purchasing decisions for any products or services. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art models. NTRODUCTION The Internet's size and importance has exploded in recent years, and it exerts a significant and growing influence on people's daily lives. Customers usually spend a substantial amount of time online, searching for information on a variety of products, communicating with others, and reading reviews.