Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2012/paper/505> ?p ?o. }
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- 505 creator arjun-mukherjee.
- 505 creator bing-liu.
- 505 type InProceedings.
- 505 label "Spotting Fake Reviewer Groups in Consumer Reviews".
- 505 sameAs 505.
- 505 abstract "Opinionated social media like product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or demote some target products. For reviews to reflect genuine user experiences and opinions, such spam reviews should be detected. Prior works on opinion spam focused on detecting fake reviews and individual spammers. However, a spammer group (a group of reviewers who work collaboratively to write fake reviews) is even more damaging and can take total control of the sentiment on the target product due to its size. This paper studies spam detection in the collaborative setting, i.e., to discover review spammer groups. The proposed method first uses a frequent itemset mining method to find a set of candidate groups. It then uses several behavioral models derived from the collusion phenomenon among spammers and relation models based on inter-relationships among products, groups, and individual reviewers to detect spammer groups. Additionally, we also built a labeled group spam dataset. To the best of our knowledge, this is the first labeled dataset on group opinion spam. Experimental results show that the proposed method is highly effective and it outperforms multiple strong baselines including both the state-of-the-art learning to rank and supervised classification algorithms.".
- 505 hasAuthorList authorList.
- 505 isPartOf proceedings.
- 505 keyword "Fake reviewers".
- 505 keyword "Group deceptive behavior".
- 505 keyword "Group opinion spam".
- 505 title "Spotting Fake Reviewer Groups in Consumer Reviews".