why amazon has a fake review problem

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2. Make certain the review received is indeed FAKE. Here are some common characteristics of fake reviews: The reviewer's name does not appear anywhere in your customer/patient database.

But in practice, it's not really clear what difference this step makes, even if you never get a response. While a nonresponse gives the green light to report the fake review, it still doesn't guarantee removal or put you in a better position than where you started. This not only puts Trustpilot near the top of many local search pages. It also gives the popular review platform outsized influence on your customers and the decision-making process.

But in practice, it's not really clear what difference this step makes, even if you never get a response. While a nonresponse gives the green light to report the fake review, it still doesn't guarantee removal or put you in a better position than where you started. This not only puts Trustpilot near the top of many local search pages. It also gives the popular review platform outsized influence on your customers and the decision-making process.

amazon.co.uk as well as the internet. It will be the site that will help me make money yet,000,000-11: '100.0 million on its average.com9 to be worth of $100TTtam: 1 percent

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Second brother testifies Jussie Smollett paid for staged attack Smollett's legal team asked Olabingo Osundairo about his previous felony conviction, which he testified Thursday was in 2012, for aggravated battery. As a convicted felon he cannot legally possess a firearm, but police found several guns when they searched their home after the alleged attack. Both brothers agreed the guns belonged to Abimbola Osundairo.

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Second brother testifies Jussie Smollett paid for staged attack Smollett's legal team asked Olabingo Osundairo about his previous felony conviction, which he testified Thursday was in 2012, for aggravated battery. As a convicted felon he cannot legally possess a firearm, but police found several guns when they searched their home after the alleged attack. Both brothers agreed the guns belonged to Abimbola Osundairo.

Second brother testifies Jussie Smollett paid for staged attack Smollett's legal team asked Olabingo Osundairo about his previous felony conviction, which he testified Thursday was in 2012, for aggravated battery. As a convicted felon he cannot legally possess a firearm, but police found several guns when they searched their home after the alleged attack. Both brothers agreed the guns belonged to Abimbola Osundairo.

By taking advantage of using the ensemble model, Mani et al. [28] introduced a supervised learning model to detect fake reviews based on unigram, and bigram features model contained two phases. In the first, Random forest, Naïve Bayes, and Support Vector Machine were used as classification algorithms. In the second phase, stacking and voting ensemble methods were used to enhance the classification model performance. The experimental results on the gold standard dataset [99] showed that the Naïve Bayes achieved the best accuracy (87.21%) in the first phase. In contrast, the stacking ensemble method performed better than voting with 87.68% accuracy. The proposed model showed the importance of using an ensemble method for detecting fake reviews. However, the proposed model did not outperform deep learning algorithms. Spammers posting reviews in an aggregate way within short periods is called Co-bursting. Based on that, a hybrid supervised machine learning method was proposed by Li et al. [81] to identify spammers. They found that reviewers' behaviour is temporal dynamic; for this reason, they proposed a labelled hidden Markov method to identify spamming through single reviewer posting time. Then expanded the technique to multi-hidden Markov to determine posting signals and behaviour with Co-bursting. They introduced the Co-bursting method to assist in detecting spammers. They used Dianping [80] real dataset, though these methods did not use any metrics to evaluate the model.

By taking advantage of using the ensemble model, Mani et al. [28] introduced a supervised learning model to detect fake reviews based on unigram, and bigram features model contained two phases. In the first, Random forest, Naïve Bayes, and Support Vector Machine were used as classification algorithms. In the second phase, stacking and voting ensemble methods were used to enhance the classification model performance. The experimental results on the gold standard dataset [99] showed that the Naïve Bayes achieved the best accuracy (87.21%) in the first phase. In contrast, the stacking ensemble method performed better than voting with 87.68% accuracy. The proposed model showed the importance of using an ensemble method for detecting fake reviews. However, the proposed model did not outperform deep learning algorithms. Spammers posting reviews in an aggregate way within short periods is called Co-bursting. Based on that, a hybrid supervised machine learning method was proposed by Li et al. [81] to identify spammers. They found that reviewers' behaviour is temporal dynamic; for this reason, they proposed a labelled hidden Markov method to identify spamming through single reviewer posting time. Then expanded the technique to multi-hidden Markov to determine posting signals and behaviour with Co-bursting. They introduced the Co-bursting method to assist in detecting spammers. They used Dianping [80] real dataset, though these methods did not use any metrics to evaluate the model.

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