The proliferation of online platforms has fundamentally altered how consumers make purchasing decisions. Online reviews, serving as valuable sources of information for potential buyers, have become integral to the success of businesses operating in the digital realm. However, the ease of posting reviews has also facilitated the emergence of deceptive reviews, which are intentionally false or misleading and designed to manipulate consumer perceptions and purchasing behavior. These reviews can take many forms, including fake positive reviews meant to boost product sales or fake negative reviews aimed at damaging competitors. The presence of these deceptive reviews undermines the trust and credibility of online platforms, harming both consumers and legitimate businesses. The economic consequences of deceptive reviews are substantial. Businesses lose revenue due to inaccurate representations of their products or services, while consumers make purchasing decisions based on misleading information. The impact extends beyond financial losses; it erodes consumer trust in online platforms and the overall digital economy. Therefore, the development of effective and accurate methods for detecting deceptive online reviews is of paramount importance to maintaining a fair and transparent e-commerce ecosystem. This research tackles the growing problem of deceptive online reviews by systematically reviewing the existing literature. We aim to provide a comprehensive overview of the different approaches used for detection, analyze their strengths and weaknesses, and highlight the critical gaps in current research. Ultimately, our review aims to guide future research efforts towards more effective and robust solutions for detecting and mitigating the impact of deceptive reviews.

The existing literature on deceptive review detection encompasses a wide range of methodologies. Early studies focused primarily on linguistic analysis, examining the textual content of reviews for indicators of deception. These methods often involve sentiment analysis, analyzing the emotional tone of reviews, or stylistic analysis, comparing the writing style of reviews to established patterns of deceptive language. However, deceptive reviewers are constantly evolving their tactics, making it challenging for solely linguistic approaches to keep pace. Behavioral analysis offers a different perspective, focusing on the patterns of reviewer behavior, such as the frequency of reviews, the consistency of reviews across different products, or the temporal patterns of review submission. Network analysis examines the connections between reviewers, products, and businesses to identify suspicious patterns of coordinated review activity. Finally, machine learning approaches have gained significant traction, employing sophisticated algorithms to analyze large datasets of reviews and identify deceptive patterns. These algorithms often incorporate features from linguistic, behavioral, and network analyses to improve detection accuracy.

This study employs a systematic literature review methodology. A comprehensive search of relevant databases (e.g., Web of Science, Scopus, IEEE Xplore, ACM Digital Library) was conducted using keywords such as "deceptive online reviews," "fake reviews," "review manipulation," "online review detection," and related terms. The search was limited to peer-reviewed journal articles and conference proceedings published in English. Inclusion criteria included studies that explicitly focused on the detection of deceptive online reviews and employed a quantitative or qualitative methodology. Exclusion criteria included studies that focused solely on sentiment analysis without addressing the detection of deceptive reviews, studies that used datasets that were not publicly available and studies that were not in English. The selected studies were carefully reviewed and analyzed based on several criteria, including the methodology employed, the type of data used, the performance metrics reported, and the limitations identified by the authors. A detailed analysis was conducted to categorize the studies based on their approaches to deceptive review detection (linguistic, behavioral, network, machine learning), the specific techniques used, and the datasets employed. The findings were then synthesized to provide a comprehensive overview of the current state of research and to identify gaps and future research directions. The review process involved multiple iterations to ensure the thoroughness and accuracy of the analysis.

Our review reveals a diverse range of methods employed for detecting deceptive reviews. Linguistic analysis methods, such as sentiment analysis and stylistic analysis, have shown some success in identifying deceptive reviews, but their effectiveness is often limited by the adaptability of deceptive reviewers. Behavioral analysis methods, focusing on reviewer behavior patterns, often complement linguistic analysis and provide additional insights. Network analysis is particularly effective in identifying coordinated review manipulation schemes, where groups of reviewers work together to create fake reviews. Machine learning approaches, leveraging techniques like deep learning and natural language processing, have emerged as promising tools for deceptive review detection, often outperforming traditional methods in terms of accuracy and efficiency. However, our analysis also highlights significant challenges in the field. The constant evolution of deceptive techniques necessitates the development of adaptive detection methods. Moreover, the reliance on specific features and data sources can limit the generalizability of many existing methods. The availability of labeled datasets for training machine learning models remains a significant bottleneck. Finally, the ethical implications of deceptive review detection must be carefully considered, as accurate detection methods can potentially infringe on user privacy or lead to unfair practices.

This comprehensive review provides a clear picture of the current state of research in deceptive online review detection. While significant progress has been made in developing effective detection methods, several challenges remain. The ongoing arms race between deceptive reviewers and detection methods requires continuous innovation. The development of adaptive and robust methods capable of handling diverse deceptive techniques is critical. Interdisciplinary approaches, integrating linguistic, behavioral, network, and machine learning methods, hold significant promise for improving detection accuracy and efficiency. Furthermore, addressing the data limitations, including the creation of larger and more diverse labeled datasets, will be crucial for advancing the field. The ethical considerations associated with detection methods also need careful attention, ensuring that the development and deployment of such methods are both effective and responsible.

Detecting deceptive online reviews is a crucial task for maintaining the integrity of online platforms and protecting both businesses and consumers. This review has comprehensively examined the existing literature, categorized the various approaches, and highlighted their strengths and limitations. Future research should focus on integrating different methodologies, developing adaptive and robust algorithms, creating larger labeled datasets, and addressing the ethical considerations related to user privacy. A multidisciplinary approach that combines expertise from computer science, linguistics, social psychology, and law is essential to fully address this multifaceted problem.

This review is limited to the published literature available up to the time of the search. The rapid development of new techniques in this field may mean that some recent advances are not included. Furthermore, the focus is primarily on quantitative studies, and the insights from qualitative studies might offer additional valuable perspectives. The evaluation of the effectiveness of various methods is often based on the performance metrics reported in the original studies, which may vary across studies and thus limit direct comparison.