Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning
With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive review...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9923374 |
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| _version_ | 1832568532603240448 |
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| author | Minjuan Zhong Zhenjin Li Shengzong Liu Bo Yang Rui Tan Xilong Qu |
| author_facet | Minjuan Zhong Zhenjin Li Shengzong Liu Bo Yang Rui Tan Xilong Qu |
| author_sort | Minjuan Zhong |
| collection | DOAJ |
| description | With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency. |
| format | Article |
| id | doaj-art-e269dc09fcf04866ace17204580d7d7c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e269dc09fcf04866ace17204580d7d7c2025-02-03T00:58:58ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99233749923374Fast Detection of Deceptive Reviews by Combining the Time Series and Machine LearningMinjuan Zhong0Zhenjin Li1Shengzong Liu2Bo Yang3Rui Tan4Xilong Qu5School of Information Technology, Hunan University of Finance and Economics, Changsha 410205, ChinaSchool of Foreign Language, Hunan University of Finance and Economics, Changsha 410205, ChinaSchool of Information Technology, Hunan University of Finance and Economics, Changsha 410205, ChinaSchool of Information Technology, Hunan University of Finance and Economics, Changsha 410205, ChinaSchool of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, ChinaSchool of Information Technology, Hunan University of Finance and Economics, Changsha 410205, ChinaWith the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency.http://dx.doi.org/10.1155/2021/9923374 |
| spellingShingle | Minjuan Zhong Zhenjin Li Shengzong Liu Bo Yang Rui Tan Xilong Qu Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning Complexity |
| title | Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning |
| title_full | Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning |
| title_fullStr | Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning |
| title_full_unstemmed | Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning |
| title_short | Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning |
| title_sort | fast detection of deceptive reviews by combining the time series and machine learning |
| url | http://dx.doi.org/10.1155/2021/9923374 |
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