DeepDynamic Clustering of Spam Reviewers using Behavior-Anomaly-based Graph Embedding
| Autor: | Xukai Zou, Feng Li, Tianchong Gao, Agnideven Palanisamy Sundar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Structure (mathematical logic)
Service (systems architecture) Information retrieval business.industry Graph embedding Computer science 02 engineering and technology Crowdsourcing Spamming 020204 information systems Credibility 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Embedding Unsupervised learning 020201 artificial intelligence & image processing business |
| Zdroj: | GLOBECOM |
| DOI: | 10.1109/globecom42002.2020.9322330 |
| Popis: | Online reviews have become an increasingly important factor in the purchase decision of a customer. However, many spammers write deceptive reviews to alter the credibility of a product/service. Often than not, these spammers exhibit group behavior, which can be exploited to differentiate them from authentic reviewers. Such behaviors are found in spammers working together as well as with crowdsourced review manipulators. The existing graph-based spammer detection approaches do not capture the dynamic and nonlinear relationship between the users. This paper aims to address this issue by introducing a method to use a deep structure embedding approach that preserves highly nonlinear structural information along with the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92% of all spam reviewers using an unsupervised learning approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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