Deceptive Reviews Research Articles

A multigrained preference analysis method for product iterative design incorporating AI-generated review detection

Online reviews significantly influence consumer purchasing decisions and serve as a vital reference for product improvement. With the surge of generative artificial intelligence (AI) technologies such as ChatGPT, some merchants might exploit them to fabricate deceptive positive reviews, and competitors may also fabricate negative reviews to influence the opinions of consumers and designers. Attention must be paid to the trustworthiness of online reviews. In addition, the opinions expressed by users are limited, and design details hidden behind reviews also affect the product usage experience. Therefore, on the basis of integrated AI-generated review detection, a multigrained user preference analysis method is proposed in this work. The proposed method utilizes pre-trained language models and designs an authenticity detection model for online reviews. Subsequently, attribute-grained preference analysis is considered a text-filling problem and uses the text-infilling objective for domain-adaptive pretraining, facilitating knowledge transfer. On the basis of the feature selection algorithm, a calculation method for the importance of product design features is proposed by introducing a random idea. The proposed method analyzes user preferences at the granularity of product attributes and design features, enabling targeted cost control and optimization in product development and guiding design decisions. Rigorous comparative and few-shot experiments substantiate the superiority of the proposed method.

BERT-Based Deceptive Review Detection in Social Media: Introducing DeceptiveBERT

BERT-Based Deceptive Review Detection in Social Media: Introducing DeceptiveBERT

Deceptive Consumer Review Analysis

: Online consumer review plays an important role in purchasing online products. Availability of various versions and models of a single product makes it difficult to choose. So, most of the customers rely on reviews to purchase the products. These deceptive reviews are being created majorly in two ways both human-crafted and AI-generated. But in today’s world most of the reviews are being deceptive either to defame the product or to make fake promotions. To solve this issue many organizations started to rely on manual labor which is a time consuming, biased and costly process. To overcome these problems, there is a need for an automatic model to detect deceptive consumer reviews. While creating this model we plan to use various Machine Learning algorithms like SVM, Multinomial Naive Bayes, Logistic Regression, Decision Trees, Random Forests and KNN to ensure the integrity and accuracy of the system. Among the above algorithms used for our models we got to observe that logistic regression seems to produce the best results for fake or real review prediction with highest accuracy (88%) and for the AI generated or human generated model we have got to see that the Support Vector Machine (SVM) produces results with highest accuracy (85%). This further led us to combine the Logistic Regression model for real or fake review detection and SVM for AI/human generated review detection which helps to find whether a review is fake or real, if at all it is fake then whether it is human generated, or AI generated.

LENS: label sparsity-tolerant adversarial learning on spatial deceptive reviews

LENS: label sparsity-tolerant adversarial learning on spatial deceptive reviews

Deep Learning-Based Truthful and Deceptive Hotel Reviews

For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports.

Classifying deceptive reviews for the cultural heritage domain: A lexicon-based approach for the Italian language

Nowadays, the spread of deceptive reviews is a problem that has reached critical dimensions, having a significant economic impact on business activities. This paper aims to estimate – at the quantitative and qualitative levels – the possibility of using particular words to disambiguate between truthful and deceptive text, focusing on reviews produced in the cultural heritage domain. For this purpose, a lexicon-based methodology has used two different lexicons: sentiment information, intensifiers, downtoners, and negation operators. As known in the literature, these elements are crucial in a classification process related to deceptiveness. The evaluation phase has considered quantitative metrics such as accuracy and F1 score and ad hoc developed metrics that consider specific linguistic parameters such as polarity and tone of voice intensifiers. A qualitative analysis of a subset of the corpus has also been carried out to understand better factors that impact the classification of deceptive review. Several linguistic features have been considered, ranging from the number of intensifiers to their type and position in phrases and sentences. A comparison between the performances of two different lexicons used has been added to the analysis.

Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance

While modern explanation methods have been shown to be inconsistent and contradictory, the explainability of black-box models nevertheless remains desirable. When the role of explanations extends from understanding models to aiding decision making, the semantics of explanations is not always fully understood – to what extent do explanations ``explain” a decision and to what extent do they merely advocate for a decision? Can we help humans gain insights from explanations accompanying correct predictions and not over-rely on incorrect predictions advocated for by explanations? With this perspective in mind, we introduce the notion of dissenting explanations: conflicting predictions with accompanying explanations. We first explore the advantage of dissenting explanations in the setting of model multiplicity, where multiple models with similar performance may have different predictions. Through a human study on the task of identifying deceptive reviews, we demonstrate that dissenting explanations reduce overreliance on model predictions, without reducing overall accuracy. Motivated by the utility of dissenting explanations we present both global and local methods for their generation.

A multiview clustering framework for detecting deceptive reviews

Online reviews, which play a key role in the ecosystem of nowadays business, have been the primary source of consumer opinions. Due to their importance, professional review writing services are employed for paid reviews and even being exploited to conduct opinion spam. Posting deceptive reviews could mislead customers, yield significant benefits or losses to service vendors, and erode confidence in the entire online purchasing ecosystem. In this paper, we ferret out deceptive reviews originated from professional review writing services. We do so even when reviewers leverage a number of pseudonymous identities to avoid the detection. To unveil the pseudonymous identities associated with deceptive reviewers, we leverage the multiview clustering method. This enables us to characterize the writing style of reviewers (deceptive vs normal) and cluster the reviewers based on their writing style. Furthermore, we explore different neural network models to model the writing style of deceptive reviews. We select the best performing neural network to generate the representation of reviews. We validate the effectiveness of the multiview clustering framework using real-world Amazon review data under different experimental scenarios. Our results show that our approach outperforms previous research. We further demonstrate its superiority through a large-scale case study based on publicly available Amazon datasets.

Checking Counterfeit Critiques on Commodities using Ensemble Classifiers Enhancing Information Credibility

The conundrum of the ubiquitous deceptive reviews has overruled the online ontology with the obsession of obscure but obligatory posting of product reviews for the customers to believe, behold and beget the online product marketing. This mandates contemporary research in the direction to delve deeper on the application and analysis of deceiving online reviews with matured and advanced AI models functional on large scale datasets to effectively and efficiently demarcate between the genuine and the sham. The research counteracts the counterfeiting product reviews via the applications, assessment and analysis of the befitting AI models - Elastic-net Classifier model based on block coordinate descent with Wordcloud and its further performance enhancement through LightGBM Trees Classifier with Grid Search and Early Stopping support, with Log-Loss as performance metric for experimentation to gain insight into the intricacies of detection, diagnosis and diminution of fake product reviews. The paper also delineates discriminative and affirmative aspects of the dataset quality, statistics, stability and standards inherent and coherent to the creation of the dataset using Large Language Models (LLMs) intrinsic to the zeitgeist juncture of recent times promoting machines to produce large scale, cost effective bogus reviews in lieu of the Amazon Mechanical Turks. The results obtained with the Log-Loss holdout score of 0.1462 conforming the LightGBM classifier proves its performance better than the Elastic-Net classifier, conforming it as better than the ROC-AUC in terms of its proximity to the prediction probability for the matching actual/true value.

The Impact of Online Reviews Manipulation on Consumer Purchase Decision Based on The Perspective of Consumers’ Perception

<p>Consumers depend on online reviews to influences their purchase decisions. On account of that,many vendors and retailers try to manipulate online reviews to mislead potential consumers to take risky purchase decisions. Many scholars have conducted a lot of research on the impact of online product reviews on consumer behavior and sales. However, the existing work are mainly based on the premise of real product reviews, but few attentions have been paid of fake ones. Based on the recognition results of deceptive reviews, this article explores whether consumers be aware or perceive it when deceptive reviews are flooding the online review system, and further analyze what influence will be imposed on final purchase decision with different perception. The empirical analysis of the questionnaire survey show that in the context of two different perceptions of consumers, deceptive reviews have significant differences in the results of purchase decisions. In addition, research also shows that consumers’ persuasive knowledge plays a moderating role between perceived deception and purchase decision.</p> <p> </p>

What makes deceptive online reviews? A linguistic analysis perspective

AbstractWith the rapid development of e-commerce, online reviews have become an important information source for consumers and e-commerce businesses. While the negative impact of deceptive online reviews has been well recognized, more research has to be done to help understand the linguistic manifestations of deceptive online reviews in order to help identify deceptive reviews and help increase the value and sustainability of e-commerce businesses. This study explores the linguistic manifestations of deceptive online reviews based on the reality monitoring theory, and then uses the data from Amazon.com online product reviews to examine perceptual cues, affective cues, detail cues, relevance cues, and cognitive cues of various deceptive online reviews. The results show that reviews for emotional catharsis are more extreme with affective cues, while perfunctory reviews often lack details with fewer prepositions and adjectives. In addition, deceptive reviews often lack relevance cues when these reviews are made to obtain the rewards provided by the vendors while paid posters tend to use more cognitive cues in deceptive reviews. Moreover, deceptive online reviews under all motives often lack perceptual cues. These findings provide a deeper understanding of the linguistic manifestations of deceptive online reviews and provide significant managerial implications for e-commerce businesses to employ high-quality online reviews for sustainable growth.

Spam Review Detection Using Machine Learning

With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviors of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviors of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews with and without features extracted from users behaviors. In both cases, we compare the performance of several classifiers; KNN, Naive Bayes (NB), SVM, Logistic Regression and Random forest. Also, different language models of n-gram in particular bi-gram and tri-gram are taken into considerations during the evaluations. The results reveal that KNN(K=7) outperforms the rest of classifiers in terms of f-score achieving best f-score 82.40%. The results show that the f-score has increased by 3.80% when taking the extracted reviewers behavioral features into consideration.

Analyzing the Impact of Augmentation Techniques on Deep Learning Models for Deceptive Review Detection: A Comparative Study

Deep Learning has brought forth captivating applications, and among them, Natural Language Processing (NLP) stands out. This study delves into the role of the data augmentation training strategy in advancing NLP. Data augmentation involves the creation of synthetic training data through transformations, and it is a well-explored research area across various machine learning domains. Apart from enhancing a model's generalization capabilities, data augmentation addresses a wide range of challenges, such as limited training data, regularization of the learning objective, and privacy protection by limiting data usage. The objective of this study is to investigate how data augmentation improves model accuracy and precise predictions, specifically using deep learning-based models. Furthermore, the study conducts a comparative analysis between deep learning models without data augmentation and those with data augmentation.

Building Fake Review Detection Model Based on Sentiment Intensity and PU Learning.

Fake review detection has the characteristics of huge stream data processing scale, unlimited data increment, dynamic change, and so on. However, the existing fake review detection methods mainly target limited and static review data. In addition, deceptive fake reviews have always been a difficult point in fake review detection due to their hidden and diverse characteristics. To solve the above problems, this article proposes a fake review detection model based on sentiment intensity and PU learning (SIPUL), which can continuously learn the prediction model from the constantly arriving streaming data. First, when the streaming data arrive, the sentiment intensity is introduced to divide the reviews into different subsets (i.e., strong sentiment set and weak sentiment set). Then, the initial positive and negative samples are extracted from the subset using the marking mechanism of selection completely at random (SCAR) and Spy technology. Second, building a semi-supervised positive-unlabeled (PU) learning detector based on the initial sample to detect fake reviews in the data stream iteratively. According to the detection results, the data of initial samples and the PU learning detector are continuously updated. Finally, the old data are continually deleted according to the historical record points, so that the training sample data are within a manageable size and prevent overfitting. Experimental results show that the model can effectively detect fake reviews, especially deceptive reviews.

DETECTING DECEPTIVE REVIEWS: AN INTEGRATED MACHINE LEARNING APPROACH

In recent years, online reviews have become a crucial factor in promoting products and services. However, the rise of fake reviews has posed a significant challenge. Businesses, marketers, and advertisers often resort to embedding fake reviews to attract customers or undermine their competitors. Deceptive reviews have become a common practice, as they serve as a means of promoting one''''s own business or tarnishing the reputation of rivals. Consequently, the identification of deceptive reviews has emerged as a critical and ongoing research area. This research paper presents a machine learning model approach to detect deceptive reviews. The study focuses on experiments conducted using a deceptive opinion spam corpus dataset, specifically targeting restaurant reviews. An n-gram model combined with max features is developed to identify deceptive content, with a particular emphasis on fake reviews. Additionally, a benchmark study is conducted to explore the performance of two different feature extraction techniques and their application in five machine learning classification techniques. The experimental findings demonstrate that the passive aggressive classifier outperforms other algorithms, achieving the highest accuracy not only in text classification but also in identifying fake reviews. Moreover, the research delves into the identification of deceptive reviews and explores diverse feature extraction and machine learning techniques to improve the model''''s accuracy.

An interpretable wide and deep model for online disinformation detection

The advent of the internet has facilitated the wide spread of online disinformation and thus poses severe threats to the trustworthiness of cyberspace. Two types of methods are proposed to detect online disinformation: traditional machine learning-based and deep learning-based, where the former is limited due to the shallow representation and the latter is hindered by its lack of interpretability. In this study, we develop a novel model named interpretable wide and deep model for text (IWDMT) for disinformation detection which incorporates the interpretability benefits of traditional machine learning and the representation advantages of deep learning. Furthermore, we advance the interpretability of existing models by utilizing neural topic models to capture topical semantic representations and the attention mechanism to extract sequential syntactic representations. The proposed IWDMT is a mixture of a generative model and a discriminative model, and we devise a novel learning algorithm for it. Experiments on deceptive reviews and fraudulent emails demonstrated the proposed IWDMT not only outperformed baselines but was also able to provide a rich set of angles of interpretation for management insights. The higher accuracy and improved interpretability in detecting online disinformation will benefit four stakeholder groups: internet users, managers, researchers, and the government.

Awareness of Deceptive Product Reviews and its Impact on Purchasing Intentions among University Youth: A Survey Study (Arabic)

Deceptive reviews are increasingly prevalent online. Considering existing literature, there are not sufficient research studies that seek to understand the role of deceptive product reviews in affecting acts of online purchasing in the Arab world. This paper attempts to investigate the perception of university youth toward deceptive online product reviews and their impact on purchasing intentions, as well as the reputational impact on the companies that offer the products and websites that carry deceptive reviews. The study followed an approach devoted to monitoring the attitudes of university youth toward review credibility through a questionnaire. Participants were requested to read reviews of two familiar items; the iPhone and a hotel. One symbolized a product, while the other denoted an experience. Participants were requested to imagine buying a mobile phone and booking a hotel in light of a range of reviews adequately perceived before moving on to answer the questions raised. The sample size was 400, representing three universities. The study found that university youth were unable to recognize fallacious reviews. Hence, there was an impact on their purchasing intentions, besides having a recognizable effect on the reputation of brands and review sites. This article is beneficial to inform youth about the importance of critical decision making in cases of fallacious information. The study also represented a reference for marketing agents to choose appropriate techniques for effective advertising. Moreover, it supports the development of online review mechanisms, particularly for authorized Saudi bodies in charge of electronic commerce regulations.

Fake Review Detection System: A Review

Fake reviews, often referred to as deceptive or dishonest reviews, have become a significant concern for both businesses and consumers (Feng et al., 2016). These reviews are deliberately crafted to mislead or manipulate the opinions of others and can be driven by various motives, such as financial gain, competition, or personal grudges (Liu et al., 2018). The impact of fake reviews on the reputation and sales of products or services can be substantial, leading consumers to make misguided purchasing decisions (Wang et al., 2017). The demand for effective methods to detect and address fake reviews has been steadily increasing, aiming to assist businesses and consumers in identifying and mitigating the influence of such reviews (Hu et al., 2019). However, detecting fake reviews presents challenges due to the diverse forms they can take and the difficulty in distinguishing them from genuine reviews (Xu et al., 2020). To tackle this challenge, researchers have developed various approaches and techniques, including machine learning, natural language processing, crowd-sourced annotation, and content-based methods (Feng et al., 2016; Liu et al., 2018). This review provides a comprehensive overview of the current state of the art in fake review detection methods, highlighting the key challenges and limitations associated with these approaches. The implications of these methods for businesses and consumers are also discussed, and future research directions are suggested to further enhance the effectiveness and reliability of fake review detection techniques.

Efficiency of automatic text generators for online review content generation

The evolution of Artificial Intelligence has led to the appearance of automatic text generators able to closely resemble human writing, endangering the development of e-commerce and the consumer confidence. Thus, it is critical to deeply understand how these text generators work to present the presence of deceptive reviews. This paper analyzes one of the most popular text generators, GPT2 (Generative Pre-trained Transformer 2), and studies its effectivity compared to human-generated reviews using previously published classifiers trained to distinguish between real and deceptive reviews. One parameter of the model is the so-called temperature, which determines how deterministic the model is. The temperature adjusts the probability distribution of the words in the model, so that a higher temperature translates into a higher degree of inventiveness in the generation of the texts. Findings reveal (i) that automatically-generated deceptive reviews worsen the accuracy of existing classifiers, this effect being accentuated by the degree of inventiveness; (ii) that their performance depends on the data used to train the generator; and (iii) that the sentiment polarity has no effect on the performance of detection classifiers.

A Deceptive Reviews Detection Method Based on Multidimensional Feature Construction and Ensemble Feature Selection

Deceptive reviews on social media and e-commerce websites are inflammatory and will significantly affect the judgment and purchase behavior of other users. At present, many researchers build models based on single text features to detect deceptive reviews. However, deceptive reviewers will deliberately imitate the text style of true reviews when writing reviews. At this time, these methods based on text features are not necessarily effective. What’s more, detection performance is limited because the category distribution is likely to be unbalanced in practice. In this work, to address these shortcomings, a deceptive review detection method based on multidimensional feature construction and ensemble feature selection is proposed. Our proposal constructs 3-D features including text feature, reviewer behavior feature, and deceptive score feature. In addition, to alleviate the impact of unbalanced category distribution, a data resampling algorithm is applied which incorporates random under-sampling (RUS) and Borderline-SMOTE algorithm. Furthermore, we integrate the results of different feature selection based on the Chi-square test, Information gain, and XGBoost feature importance. Our method addresses the limitation of single dimension features and can provide useful detection. Experimental results area under the curve (AUC, Macro Average Precision, and weighted F1-score) show that the proposed method performs well in the task of deceptive review detection on two Amazon datasets. Compared with other advanced methods, our method achieves additional performance gains in the case of poor text quality and datasets with unbalanced category distribution.