SC-Com: Spotting Collusive Community in Opinion Spam Detection
| Autor: | Chong-kwon Kim, Hyungho Byun, Sihyun Jeong |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
Computer science Process (engineering) Behavioral pattern 02 engineering and technology Library and Information Sciences Management Science and Operations Research Spotting Computer Science Applications Opinion spam 020204 information systems 0202 electrical engineering electronic engineering information engineering Media Technology Graph (abstract data type) 020201 artificial intelligence & image processing Abnormality Information Systems |
| Zdroj: | Information Processing & Management. 58:102593 |
| ISSN: | 0306-4573 |
| DOI: | 10.1016/j.ipm.2021.102593 |
| Popis: | In many cases, our decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers’ opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, we can spot the possibility to detect them from their collusiveness. In this paper, we propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, we extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. We show that our method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, our approach showed prominent performance while only considering primary data such as time and ratings. |
| Databáze: | OpenAIRE |
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