Open Access
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
Article Number 02005
Number of page(s) 14
Section Data Science
Published online 09 August 2023
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  12. Jianrong Yao & Yuan Zheng & Hui Jiang (2021), “An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization”, DOI: 10.1109/ACCESS.2021.3051174, China. [Google Scholar]
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  14. Rami Mohawesh & Shuxiang Xu & Son N. Tran & Robert Ollington & Matthew Springer & Yaser Jararweh & Sumbal Maqsood (2021), “Fake Reviews Detection: A Survey”, DOI: 10.1109/ACCESS.2021.3075573, Australia. [Google Scholar]
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  16. Punit Rathore & Jayesh Soni & Nagarajan Prabakar & Marimuthu Palaniswami & Paolo Santi (2021), “Identifying Groups of Fake Reviewers Using a Semisupervised Approach”, DOI: 10.1109/TCSS.2021.3085406. [Google Scholar]
  17. Jingdong Wang & Haitao Kan & Fanqi Meng & Qizi Mu & Genhua Shi & Xixi Xiao (2020), “Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training”, DOI: 10.1109/ACCESS.2020.3028588, China. [Google Scholar]
  18. Wenqian Liu & Jingsha He & Song Han & Fangbo Cai & Zhenning Yang & Nafei Zhu (2019), “A Method for the Detection of Fake Reviews Based on Temporal Features of Reviews and Comments”, DOI: 10.1109/EMR.2019.2928964, China. [Google Scholar]
  19. Estée Van Der Walt & An Eloff (2018), “Using Machine Learning to Detect Fake Identities: Bots vs Humans”, DOI: 10.1109/ACCESS.2018.2796018, South Africa. [Google Scholar]
  20. Lu Zhang & Zhiang Wu & Jie Cao (2018), “Detecting Spammer Groups From Product Reviews: A Partially Supervised Learning Model”. DOI: 10.1109/ACCESS.2017.2784370, China [Google Scholar]

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