EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 56 (2023) ITM Web Conf., 56 (2023) 02005 References
Open Access
Issue
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
DOI https://doi.org/10.1051/itmconf/20235602005
Published online 09 August 2023
  1. Zhang Shunxiang; Zhu Aoqiang; Zhu Guangli; Wei Zhongliang; Li KuanChing (2023) “Building Fake Review Detection Model Based on Sentiment Intensity and PU Learning” DOI: 10.1109/TNNLS.2023.3234427 [Google Scholar]
  2. Thi-Kim-Hien Le & Yi-Zhen Li & Sheng Tun Li (2022), “Do Reviewers’ Words and Behaviours Help Detect Fake Online Reviews and Spammers? Evidence From a Hierarchical Model”, DOI: 10.1109/ACCESS.2022.3167511, South Vietnam. [Google Scholar]
  3. Mujahed Abdulqader & Abdallah Namoun & Azed Alsaawy (2022), “Fake online reviews: A unified detection model using deception theories”, DOI: 10.1109/ACCESS.2022.3227631, Saudi Arabia. [Google Scholar]
  4. Noureddine Seddari & Abdelouahid Derhab & Mohamed Belaoued & Waleed Halboo & Jalal Al-Muhtadi & Abdelghani Bouras (2022), “A Hybrid Linguistic and Knowledge-Based Analysis Approach for Fake News Detection on Social Media”, DOI: 10.1109/ACCESS.2022.3181184. [Google Scholar]
  5. Hina Tufail & M. Usman Ashraf & Khalid Alsubhi & Hani Moaiteq Aljahdali (2022), “The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection”, DOI: 10.1109/ACCESS.2022.3152806, Arab. [Google Scholar]
  6. L. Chen and Q. Li, “How Does fake review Influence E-Commerce Platform Revenue?,” 2022 23rd Asia- Pacific Network Operations and Management Symposium (APNOMS), Takamatsu, Japan, 2022, pp. 1-4, DOI: 10.23919/APNOMS56106.2022.9919902. [Google Scholar]
  7. R. Agarwal and D. K. Sharma, “Detecting Fake Reviews using Machine learning techniques: a survey,” 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2022, pp. 1750-1756, DOI: 10.1109/ICACITE53722.2022.9823633. [Google Scholar]
  8. V. P. Sumathi, S. M. Pudhiyavan, M. Saran and V. N. Kumar, “Fake Review Detection Of E-Commerce Electronic Products Using Machine Learning Techniques,” 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2021, pp. 1-5, DOI: 10.1109/ICAECA52838.2021.9675684. [Google Scholar]
  9. P. Devika, A. Veena, E. Srilakshmi, A. R. Reddy and E. Praveen, “Detection of fake reviews using NLP &Sentiment Analysis,” 2021 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatre, India, 2021, pp. 1534-1537, DOI: 10.1109/ICCES51350.2021.9489210. [CrossRef] [Google Scholar]
  10. D. Agnihotri, S. Ahmed, D. Darekar and C. Gadkari, “Examination of Fake Reviews of Product and Approach to Filter them from Legitimate Reviews,” 2021 IEEE Pune Section International Conference (PuneCon), Pune, India, 2021, pp. 1-4, DOI: 10.1109/PuneCon52575.2021.9686477. [Google Scholar]
  11. Meiling Liu & Yue Shang & Qi Yue & Jiyun Zhou (2021), “Detecting Fake Reviews Using Multidimensional Representations With FineGrained Aspects Plan”, DOI: 10.1109/ACCESS.2020.3047947, China. [Google Scholar]
  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]
  13. Songkai Tang & Luhua Jin & Fan Cheng (2021), “Fraud Detection in Online Product Review Systems via Heterogeneous Graph Transforme”, DOI: 10.1109/ACCESS.2017.DOI, China. [Google Scholar]
  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]
  15. Matthew Carter & Michail Tsikerdekis & Sherali Zeadally (2021), “Approaches for Fake Content Detection: Strengths and Weaknesses to Adversarial Attacks”, DOI: 10.1109/MIC.2020.3032323, USA. [Google Scholar]
  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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.