Creating and detecting fake reviews of online products
<p>Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had pa...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2022
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513506168012800 |
|---|---|
| author | Joni Salminen (7434770) |
| author2 | Chandrashekhar Kandpal (19517671) Ahmed Mohamed Kamel (19517674) Soon-gyo Jung (7434773) Bernard J. Jansen (7434779) |
| author2_role | author author author author |
| author_facet | Joni Salminen (7434770) Chandrashekhar Kandpal (19517671) Ahmed Mohamed Kamel (19517674) Soon-gyo Jung (7434773) Bernard J. Jansen (7434779) |
| author_role | author |
| dc.creator.none.fl_str_mv | Joni Salminen (7434770) Chandrashekhar Kandpal (19517671) Ahmed Mohamed Kamel (19517674) Soon-gyo Jung (7434773) Bernard J. Jansen (7434779) |
| dc.date.none.fl_str_mv | 2022-09-20T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jretconser.2021.102771 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Creating_and_detecting_fake_reviews_of_online_products/26889430 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Machine learning Fake reviews Detectione-commercee WOM Marketing |
| dc.title.none.fl_str_mv | Creating and detecting fake reviews of online products |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.</p><h2>Other Information</h2> <p> Published in: Journal of Retailing and Consumer Services<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jretconser.2021.102771" target="_blank">https://dx.doi.org/10.1016/j.jretconser.2021.102771</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_50c77dbd7a525facbf6fe5fbfc66f25d |
| identifier_str_mv | 10.1016/j.jretconser.2021.102771 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26889430 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Creating and detecting fake reviews of online productsJoni Salminen (7434770)Chandrashekhar Kandpal (19517671)Ahmed Mohamed Kamel (19517674)Soon-gyo Jung (7434773)Bernard J. Jansen (7434779)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningFake reviewsDetectione-commerceeWOMMarketing<p>Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.</p><h2>Other Information</h2> <p> Published in: Journal of Retailing and Consumer Services<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jretconser.2021.102771" target="_blank">https://dx.doi.org/10.1016/j.jretconser.2021.102771</a></p>2022-09-20T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jretconser.2021.102771https://figshare.com/articles/journal_contribution/Creating_and_detecting_fake_reviews_of_online_products/26889430CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268894302022-09-20T12:00:00Z |
| spellingShingle | Creating and detecting fake reviews of online products Joni Salminen (7434770) Information and computing sciences Artificial intelligence Data management and data science Machine learning Fake reviews Detectione-commercee WOM Marketing |
| status_str | publishedVersion |
| title | Creating and detecting fake reviews of online products |
| title_full | Creating and detecting fake reviews of online products |
| title_fullStr | Creating and detecting fake reviews of online products |
| title_full_unstemmed | Creating and detecting fake reviews of online products |
| title_short | Creating and detecting fake reviews of online products |
| title_sort | Creating and detecting fake reviews of online products |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Fake reviews Detectione-commercee WOM Marketing |