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Fake Reviews Detection: A Survey Positive reviews bring big financial gains, while negative reviews often exert a negative financial effect [47], [48]. Consequently, with customers becoming increasingly influential to the marketplace, there is a growing trend towards relying on customers' opinions to reshape businesses by enhancing products, services, and marketing [52]–​[54]. For example, when several customers who purchased a specific model of Acer laptop posted reviews complaining about the low display quality, the manufacturer was inspired to produce a higher-resolution version of the laptop. The way consumers openly express and use their feedback has contributed to issues with websites containing customer reviews. Social media (Twitter, Facebook, etc.) allows anyone to freely post feedback or critiques of any company at any time with no obligations or limits. The lack of restrictions, in turn, leads certain companies to use social media to unfairly promote their goods, brands or shops, or to unfairly criticise those of their rivals. For example, suppose a few consumers who bought a specific digital camera posted negative reviews on image quality. These reviews portray the digital camera unfavourably to the public. Thus, the camera manufacturer might employ an individual or team to post fake positive reviews about the camera. Similarly, in order to promote the company, the producer might ask the hired persons to post negative comments about competitors' products. Reviews published by people who have not personally encountered the items being reviewed are considered fake reviews [11]. Accordingly, a person who posts fake reviews is called a spammer [11]. When the spammer works with other spammers to achieve a specific goal, the spammers are called a group of spammers [11]. Similarly, Lin et al. [12] introduced a classification model to detect fake reviews in a cross-domain environment based on a Sparse Additive Generative Model (SAGE), which is created based on the Bayesian generative model [136]. The model is a combination of a generalized additive model and topic modelling [137]. They used linguistic query and word account (LIWC), POS, and unigram techniques as features to detect fake reviews in cross-domains. The proposed model could capture different aspects such as fake vs. truthful and positive vs. negative. They used the AMT dataset [77] which consisting of three domain reviews (Hotels, Doctors, and Restaurants) to evaluate the proposed model. The experimental results showed that the accuracy of the classification using unigram was 65%. The accuracy of two class classifications (Turker and Employee reviews) using unigram was 76.1%. The accuracy on cross-domain using unigram, POS, and LIWC separately were 77%, 74.6%, and 74.2%, respectively, on the restaurant domain. The accuracy on cross-domain using unigram, POS, and LIWC separately using Doctor domain were: 52%, 63.4%, and 64.7%. However, the proposed model failed in capturing the semantic information of the sentence. In related work, Hernández-Castañeda et al. [29] investigated the efficiency of using SVN (Support Vector Network) in classification tasks to detect fake reviews in one, mixed and cross-domains. They used the LIWC, Word space model (WSM), and latent Dirichlet Allocation (LDA) techniques as a feature extraction method. They evaluated the proposed model on three datasets; the DeRev dataset [89], OpSpam dataset [77] and Opinions dataset [138]. The results compared to the previous works [77], [89], [138] showed that a combination of WSM and LDA achieved the best results in one domain with an accuracy of 90.9% on the OpSpam dataset, 94.9% on DeRev dataset, 87.5% on Abortion dataset, 87% on Best Friend dataset and 80% on Death Penalty dataset. There was also an accuracy of 76.3% in a mixed domain compared to the Naïve Bayes classifier. However, the proposed model did not achieve the best results on cross-domain compared to state-of the-art methods. The performance was good in one domain and mix domain and poor in cross-domain because they used the dataset for testing and combined the remaining dataset for training. This suggests that a deep neural network is probably more appropriate to improve fake review detection in a cross-domain by improving the learning presentation. Show All We believe this survey will be valuable for researchers with a comprehensive understanding of this field's key aspects. It elucidates the most notable advances and sheds some light on expected future directions.

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