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  Li J, Ott M, Cardie C, Hovy E (2014) Towards a general rule for identifying deceptive opinion spam. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1566–1576, Baltimore, Maryland, USA, June 23-25 2014. ACL There are no clear indications or signals from the text of the two reviews that indicate to the casual reader that the first review is real while the second is a fake. Nevertheless, guides provided by the ConsumeristFootnote 2 and MoneyTalksNewsFootnote 3 websites offer tips to help consumers spot fake reviews. A computer scientist might seek to utilize this logic when training data mining and machine learning algorithms to find these features in the review that will determine if it is real or fake.

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  Li J, Ott M, Cardie C, Hovy E (2014) Towards a general rule for identifying deceptive opinion spam. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1566–1576, Baltimore, Maryland, USA, June 23-25 2014. ACL There are no clear indications or signals from the text of the two reviews that indicate to the casual reader that the first review is real while the second is a fake. Nevertheless, guides provided by the ConsumeristFootnote 2 and MoneyTalksNewsFootnote 3 websites offer tips to help consumers spot fake reviews. A computer scientist might seek to utilize this logic when training data mining and machine learning algorithms to find these features in the review that will determine if it is real or fake.

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  Li J, Ott M, Cardie C, Hovy E (2014) Towards a general rule for identifying deceptive opinion spam. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1566–1576, Baltimore, Maryland, USA, June 23-25 2014. ACL There are no clear indications or signals from the text of the two reviews that indicate to the casual reader that the first review is real while the second is a fake. Nevertheless, guides provided by the ConsumeristFootnote 2 and MoneyTalksNewsFootnote 3 websites offer tips to help consumers spot fake reviews. A computer scientist might seek to utilize this logic when training data mining and machine learning algorithms to find these features in the review that will determine if it is real or fake.

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  The Facebook groups Oak discovered were marketplaces where reviews and ratings were bought and sold. Agents shared lists of products available for reviewers-one of the spreadsheets Oak saw had more than 10,000 products on it-and while most options are relatively cheap, there are pricier ticket items like robot vacuums and even a $500 treadmill. After that first review, the ads kept coming. The scale of the operation piqued his interest, so Oak set up a few sock puppet Facebook accounts and began joining groups offering free Amazon products for review. Some of these groups had thousands of members with agents from countries like Pakistan, Bangladesh, and India working for sellers in China to secure reviews on Amazon in the US and Europe.

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  The Facebook groups Oak discovered were marketplaces where reviews and ratings were bought and sold. Agents shared lists of products available for reviewers-one of the spreadsheets Oak saw had more than 10,000 products on it-and while most options are relatively cheap, there are pricier ticket items like robot vacuums and even a $500 treadmill. After that first review, the ads kept coming. The scale of the operation piqued his interest, so Oak set up a few sock puppet Facebook accounts and began joining groups offering free Amazon products for review. Some of these groups had thousands of members with agents from countries like Pakistan, Bangladesh, and India working for sellers in China to secure reviews on Amazon in the US and Europe.

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