so many fake reviews on amazon>so many fake reviews on amazon

so many fake reviews on amazon

so many fake reviews on amazon

In a recent presentation to the Legislature's Joint Interim Committee on Gambling Regulation, Sport Oregon spokesman Nathan Nayman offered a different critique from Martz's. He notes other states with sports betting generate five or six times Oregon's per capita revenue.

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Recently, Jain et al. [41] proposed hierarchical CNN-GRN deep learning methods, and Multi instant learning (MIL) methods were proposed to handle the variable lengths of reviews in fake reviews detection. A three-layer CNN was utilized to extract localized n-gram features. In contrast, GRN was employed to learn semantic dependencies between the extracted features from CNN. In several instances, the input text is divided into multiple instances, and the last instance is discarded if the word length is less than fifteen. The proposed model was evaluated on multiple benchmark datasets, including four-city dataset [163], Yelp Zip dataset [72], Deceptive Spam Corpus [77], Drug Review dataset [164] and Large Movie Review dataset [165]. The experimental results showed that MIL and CNN-GRN performed better than classical CNN and RNN on all datasets. However, the proposed model works well with short text only. Instead of relying strongly on expert knowledge to recognize fake reviews with a new perspective, Wang et al. [76] introduced a new spam detection model based on the relations between reviewers and products. They constructed a 3-mode tensor based on the relations generated from two entities; tensor factorization algorithms called RESCAL [168] were utilized to learn the vector representation of product and reviewers automatically. Lastly, the final concatenated representation of the review is fed into a support vector machine classifier. The Yelp Chi dataset [8] was used to evaluated the proposed model. The results showed that the proposed model outperformed the state-of-the-art method [8], [72] with an 85.9% accuracy on the hotel domain and 87.8% on the restaurant domain. The proposed model showed that the relations between reviewer and product are critical to enhance the classification model performance. To detect a single fake review, Wang et al. [17] introduced a multi-dimensional time series model. A unique index was introduced to determine reviewers' credibility by taking trustworthiness and expertise together. The ranking method was introduced to summarize all spammers in various dimensions to detect abnormal time series aspects. When a single fake review happens in the time series, the window size time is reduced. The results showed that the proposed model was useful with human assessment compared to the average RHR on the datasets, consisting of 408,469 reviews from different websites. They discovered that many reviews were posted at the same time on different days between 2009-2010. However, the proposed model did not use metrics like recall, precision, F1 measure, and accuracy to evaluate the method. Heydari et al. [5] introduced a pattern recognition method to identify deceptive reviews fallen in suspicious periods based on metadata and rating deviation features. A time series is constructed to determine oscillations in several reviews for each product. A sliding window is utilized to capture the suspicious periods and find the patterns. The results on real datasets from showed that the proposed method performed well in fake review detection with an 86% F-measure. Despite the advantages of the proposed method, it focused on suspicious periods rather than reducing expensive computations in the scoring phase. Furthermore, Hand annotated techniques need many human resources, and adding metadata such as IP address can boost the proposed model's performance.

If you sign up for a rewards card and provide information to the merchant, such as your name, postal code, email address, and phone number, Apple will receive notification of the signup, but the information that you share will be sent directly from your device to the merchant and is treated in accordance with the merchant's privacy policy. To verify your identity, you may be asked to provide information including your name and address to the bank and their identity verification service provider. This information is used only for fraud prevention and to comply with U.S. financial regulations. Your name and address is securely stored by the partner bank and Apple Payments Inc., but any additional information that you're asked to provide - such as social security number, date of birth, answers to questions (e.g., confirm street name you have previously lived on), or a copy of your government ID - can't be read by Apple.

so many fake reviews on amazon

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