Enhancing Trust in Online Reviews: A Cross-Domain Fake Review Detection Framework Using Generative AI

Online reviews influence consumer decisions in e-commerce but are threatened by deceptive content. Detecting these fake reviews across domains remains challenging due to limited labelled data, class imbalance, and domain-specific bias. This work proposes CORAL, a unified retrieval-augmented framework for cross-domain fake review detection that mitigates source-domain bias through unified masking and generates targetaware counterfactuals via retrieval grounding with orthogonal alignment. Unlike prior domain-adaptation approaches, CORAL enables zero-shot classification without manual annotation. Evaluated across twelve cross-domain tasks on benchmark ecommerce datasets, CORAL achieved average gains of 21.74 % in accuracy and 24.25 % in F1-score over strong baselines while improving trustworthiness and robustness. It demonstrated stability across random seeds, resilience to moderate noise, reduced hallucination, and competitive runtime efficiency. Additionally, CORAL enabled the annotation of a novel healthcare review dataset, addressing scarcity of labelled data.