Browsing by Author "Duma, Ramadhani Ally"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item A deep feature interaction and fusion model for fake review detection: Advocating heterogeneous graph convolutional network(Elsevier BV, 2024) Duma, Ramadhani Ally; Niu, Zhendong; Nyamawe, Ally S.; Manjotho, Ali AsgharIn contemporary real-world scenarios, opinion spammers are hired to fabricate reviews that unfairly promote or demote particular products or services for personal gain. Although considerable attention has been devoted to addressing the problem, existing approaches often overlook the heterogeneous nature of reviewer–product interactions. Specifically, the correlation between review text (comments) and overall ratings, which provides various latent rich information to expose fake reviews, remains inadequately explored. Current methodologies focus on limited interactions, such as reviewer–review, product–review, or reviewer–product interactions, while neglecting significant aspects like reviewer–review–product and reviewer–rating–product interactions, leading to inadequate classifier performance. Motivated by this observation, this study proposes a novel Deep Feature Interaction and Fusion Model (DFIFM) whose ideas are five-folds: (a) constructing a reviewer–product interaction bipartite graph that represents heterogeneous feature node interactions through review text and overall rating values; (b) recognizing the existing mutual interactive relationship between review text and overall rating features, we construct a unified GCN to gain additional insights into feature relationships and capture mutual heterogeneous interactions between nodes; (c) to handle the encoding of unstructured review text features as edge attributes, we adopt a convolutional neural network (CNN); (d) attention mechanisms and fusion techniques are employed to capture interdependencies among reviewer–product latent features; and (e) a Multilayer Perceptron (MLP) utilizes the resulting latent feature representation for review classification. Experimental results on three publicly available datasets demonstrate its superiority over state-of-the-art baselines.Item Fake review detection techniques, issues, and future research directions: a literature review(Springer Science and Business Media LLC, 2024) Duma, Ramadhani Ally; Niu, Zhendong; Nyamawe, Ally S.; Tchaye-Kondi, Jude; Jingili, Nuru; Yusuf, Abdulganiyu Abdu; Deve, Augustino FaustinoRecently, the impact of product or service reviews on customers' purchasing decisions has become increasingly significant in online businesses. Consequently, manipulating reviews for fame or profit has become prevalent, with some businesses resorting to paying fake reviewers to post spam reviews. Given the importance of reviews in decision-making, detecting fake reviews is crucial to ensure fair competition and sustainable e-business practices. Although significant efforts have been made in the last decade to distinguish credible reviews from fake ones, it remains challenging. Our literature review has identified several gaps in the existing research: (1) most fake review detection techniques have been proposed for high-resource languages such as English and Chinese, and few studies have investigated low-resource and multilingual fake review detection, (2) there is a lack of research on deceptive review detection for reviews based on language code-switching (code-mix), (3) current multi-feature integration techniques extract review representations independently, ignoring correlations between them, and (4) there is a lack of a consolidated model that can mutually learn from review emotion, coarse-grained (overall rating), and fine-grained (aspect ratings) features to supplement the problem of sentiment and overall rating inconsistency. In light of these gaps, this study aims to provide an in-depth literature analysis describing strengths and weaknesses, open issues, and future research directions.