Detecting fraudulent product reviews with enhanced accuracy

The rise of e-commerce has brought unprecedented convenience to consumers, but it has also created fertile ground for deceptive practices in online marketplaces. A growing body of research is now focusing on the detection of fake or misleading product reviews, often referred to as spam reviews. These are deliberately written to either unfairly promote a product or damage a competitor’s reputation.

These reviews frequently use fabricated profiles or carefully crafted language, making them difficult to distinguish from genuine customer feedback. Moreover, the use of large language models, colloquially known as generative AI, are now being used to generate authentic-seeming spam reviews.

The impact of spam reviews is significant. Consumers may be persuaded to purchase low-quality goods, while legitimate businesses suffer reputational harm. Ultimately, this might erode trust in digital marketplaces. However, distinguishing between authentic opinions and deceptive ones is difficult.

For their article published in the International Journal of Services, Economics and Management researchers have turned to computational opinion mining, which involves analyzing text to extract sentiment and meaning, to detect patterns indicative of fraudulent activity.

Traditional techniques include filtering for suspicious keywords, monitoring abnormal posting patterns, assessing reviewer credibility, and employing verification tools such as anti-spam CAPTCHAs.

More recently, advances in machine learning (ML) and natural language processing (NLP), which allows a computer to interpret human language, have enabled automated systems to detect the subtle linguistic and contextual cues that often reveal fabricated content.

The researchers explain that central to their approach is the creation of ground truth datasets. These are curated examples of real and fake reviews. These datasets provide a reference for training machine learning models to recognize subtle indicators of deception, including unusual writing styles, sentiment inconsistencies, or anomalies in sentence structure.

The new approach then combines multiple algorithms into a hybrid classifier. A deep learning framework, such as a convolutional neural network (CNN), which is adept at identifying complex patterns, is paired with a traditional statistical classifier. The accuracy rate of this hybrid is between 96% and 99% when tested on standard datasets.

As global e-commerce continues to expand, accurate spam detection systems will become increasingly important in maintaining the reliability of digital marketplaces, reinforcing transparency and trustworthiness.