Using supervised learning to classify authentic and fake online reviews
Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and f...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/107209 http://hdl.handle.net/10220/25330 http://dx.doi.org/10.1145/2701126.2701130 |
| _version_ | 1826130196372127744 |
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| author | Banerjee, Snehasish Chua, Alton Yeow Kuan Kim, Jung-Jae |
| author2 | Wee Kim Wee School of Communication and Information |
| author_facet | Wee Kim Wee School of Communication and Information Banerjee, Snehasish Chua, Alton Yeow Kuan Kim, Jung-Jae |
| author_sort | Banerjee, Snehasish |
| collection | NTU |
| description | Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising. |
| first_indexed | 2024-10-01T07:52:31Z |
| format | Conference Paper |
| id | ntu-10356/107209 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:52:31Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/1072092019-12-06T22:26:44Z Using supervised learning to classify authentic and fake online reviews Banerjee, Snehasish Chua, Alton Yeow Kuan Kim, Jung-Jae Wee Kim Wee School of Communication and Information 9th International Conference on Ubiquitous Information Management and Communication DRNTU::Library and information science::Libraries::Information systems DRNTU::Social sciences::Communication::Communication theories and models Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising. Published version 2015-04-07T07:48:24Z 2019-12-06T22:26:44Z 2015-04-07T07:48:24Z 2019-12-06T22:26:44Z 2015 2015 Conference Paper Banerjee, S., Chua, A. Y. K., & Kim, J.-J. (2015). Using supervised learning to classify authentic and fake online reviews. Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication. https://hdl.handle.net/10356/107209 http://hdl.handle.net/10220/25330 http://dx.doi.org/10.1145/2701126.2701130 en © 2015 Association for Computing Machinery. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, Association for Computing Machinery. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2701126.2701130]. 7 p. application/pdf |
| spellingShingle | DRNTU::Library and information science::Libraries::Information systems DRNTU::Social sciences::Communication::Communication theories and models Banerjee, Snehasish Chua, Alton Yeow Kuan Kim, Jung-Jae Using supervised learning to classify authentic and fake online reviews |
| title | Using supervised learning to classify authentic and fake online reviews |
| title_full | Using supervised learning to classify authentic and fake online reviews |
| title_fullStr | Using supervised learning to classify authentic and fake online reviews |
| title_full_unstemmed | Using supervised learning to classify authentic and fake online reviews |
| title_short | Using supervised learning to classify authentic and fake online reviews |
| title_sort | using supervised learning to classify authentic and fake online reviews |
| topic | DRNTU::Library and information science::Libraries::Information systems DRNTU::Social sciences::Communication::Communication theories and models |
| url | https://hdl.handle.net/10356/107209 http://hdl.handle.net/10220/25330 http://dx.doi.org/10.1145/2701126.2701130 |
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