Detecting review spammer groups based on generative adversarial networks-Reference-Cited by-同舟云学术

Detecting review spammer groups based on generative adversarial networks

Published:2022-08 Issue: Volume:606 Page:819-836
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Zhang Fuzhi,Yuan Shuai,Zhang Peng,Chao Jinbo,Yu Hongtao

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference46 articles.

1. State-of-art approaches for review spammer detection: a survey;Dewang;J. Intell. Inform. System,2018

2. Detection of review spam: A survey;Heydari;Expert Syst. Appl.,2015

3. Spotting fake reviewer groups in consumer reviews;Mukherjee,2012

4. Uncovering collusive spammers in Chinese review websites;Xu,2013

5. Survey on fake review detection research;Li;Chin. J. Computers,2018

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing fairness of trading environment: discovering overlapping spammer groups with dynamic co-review graph optimization;Cybersecurity;2024-06-04

2. A Robust Rating Aggregation Method based on Rater Group Trustworthiness for Collusive Disturbance;Information Systems Frontiers;2024-05-06

3. Detecting malicious reviews and users affecting social reviewing systems: A survey;Computers & Security;2023-10

4. NRWalk2Vec-HIN: spammer group detection based on heterogeneous information network embedding over social media;The Journal of Supercomputing;2023-07-24

5. Developing a Deep Learning Model to Classify Malicious User on Social Networks;2023 International Conference on Communication, Circuits, and Systems (IC3S);2023-05-26