Name
Abstract | ||
|---|---|---|
Spammers generate fake reviews to influence the reputation of products. By grouping together, spammers can dramatically alter how products are perceived. Different from previous research, which has mostly used behavioral indicators and structural indicators, we propose a new perspective on spammer detection. In our approach, we portray reviewers as a comment-based reviewer network through a new collusion similarity measure, divide reviewers into different communities using an effective community detection method and separate spammer communities from normal reviewer communities through network structure. We find that spammer communities have different network structural features from normal reviewer communities, a high clustering coefficient and high self-similarity. In our experiments, we show that our method achieves a detection accuracy of 94.59% - substantially higher than the current state-of-the-art methods which achieve an 80.00% accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-030-00916-8_61 | Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering |
Keywords | Field | DocType |
Fake reviews,Spammer community,The comment-based reviewer network,Network structural features | Similarity measure,Computer science,Computer network,Artificial intelligence,Clustering coefficient,Fake reviews,Machine learning,Collusion,Spamming,Reputation,Network structure | Conference |
Volume | ISSN | Citations |
252 | 1867-8211 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Zhou | 1 | 4 | 1.81 |
| Meng Liu | 2 | 39 | 18.70 |
| Yajun Zhang | 3 | 0 | 1.01 |