Detection of review spam: A survey
In recent years, online reviews have become the most important resource of customers' opinions. These reviews are used increasingly by individuals and organizations to make purchase and business decisions. Unfortunately, driven by the desire for profit or publicity, fraudsters have produced dec...
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Elsevier Ltd.
2015
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my.utm.582582022-04-07T03:37:24Z http://eprints.utm.my/id/eprint/58258/ Detection of review spam: A survey Heydari, Atefeh Tavakoli, Mohammad Ali Salim, Naomie Heydari, Zahra QA75 Electronic computers. Computer science In recent years, online reviews have become the most important resource of customers' opinions. These reviews are used increasingly by individuals and organizations to make purchase and business decisions. Unfortunately, driven by the desire for profit or publicity, fraudsters have produced deceptive (spam) reviews. The fraudsters' activities mislead potential customers and organizations reshaping their businesses and prevent opinion-mining techniques from reaching accurate conclusions. The present research focuses on systematically analyzing and categorizing models that detect review spam. Next, the study proceeds to assess them in terms of accuracy and results. We find that studies can be categorized into three groups that focus on methods to detect spam reviews, individual spammers and group spam. Different detection techniques have different strengths and weaknesses and thus favor different detection contexts. Elsevier Ltd. 2015 Article PeerReviewed Heydari, Atefeh and Tavakoli, Mohammad Ali and Salim, Naomie and Heydari, Zahra (2015) Detection of review spam: A survey. Expert Systems With Applications, 42 (7). pp. 3634-3642. ISSN 0957-4174 htp://dx.doi.org/10.1016/j.eswa.2014.12.029 DOI:10.1016/j.eswa.2014.12.029 |
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QA75 Electronic computers. Computer science Heydari, Atefeh Tavakoli, Mohammad Ali Salim, Naomie Heydari, Zahra Detection of review spam: A survey |
| description |
In recent years, online reviews have become the most important resource of customers' opinions. These reviews are used increasingly by individuals and organizations to make purchase and business decisions. Unfortunately, driven by the desire for profit or publicity, fraudsters have produced deceptive (spam) reviews. The fraudsters' activities mislead potential customers and organizations reshaping their businesses and prevent opinion-mining techniques from reaching accurate conclusions. The present research focuses on systematically analyzing and categorizing models that detect review spam. Next, the study proceeds to assess them in terms of accuracy and results. We find that studies can be categorized into three groups that focus on methods to detect spam reviews, individual spammers and group spam. Different detection techniques have different strengths and weaknesses and thus favor different detection contexts. |
| format |
Article |
| author |
Heydari, Atefeh Tavakoli, Mohammad Ali Salim, Naomie Heydari, Zahra |
| author_facet |
Heydari, Atefeh Tavakoli, Mohammad Ali Salim, Naomie Heydari, Zahra |
| author_sort |
Heydari, Atefeh |
| title |
Detection of review spam: A survey |
| title_short |
Detection of review spam: A survey |
| title_full |
Detection of review spam: A survey |
| title_fullStr |
Detection of review spam: A survey |
| title_full_unstemmed |
Detection of review spam: A survey |
| title_sort |
detection of review spam: a survey |
| publisher |
Elsevier Ltd. |
| publishDate |
2015 |
| url |
http://eprints.utm.my/id/eprint/58258/ |
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1729703227844198400 |
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13.211869 |