Identifying Groups of Fake Reviewers Using a Semisupervised Approach-Reference-Cited by-同舟云学术

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Identifying Groups of Fake Reviewers Using a Semisupervised Approach

Published:2021-12 Issue:6 Volume:8 Page:1369-1378
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Rathore Punit^ORCID,Soni Jayesh,Prabakar Nagarajan,Palaniswami Marimuthu^ORCID,Santi Paolo^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/6570650/9632444/09451179.pdf?arnumber=9451179

Cited by 25 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. Graph neural network-based attention mechanism to classify spam review over heterogeneous social networks;The Journal of Supercomputing;2024-08-29

2. FakeX: A Framework for Detecting Fake Reviews of Browser Extensions;Proceedings of the 19th ACM Asia Conference on Computer and Communications Security;2024-07

3. Collusive spam detection from Chinese community question answering sites: A collective classification framework;Information Sciences;2024-05

4. Public Perception of Online P2P Lending Applications;Journal of Theoretical and Applied Electronic Commerce Research;2024-03-01

5. Detecting Fake Online Reviews: An Unsupervised Detection Method With a Novel Performance Evaluation;International Journal of Electronic Commerce;2024-01-02

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