Fake review detection techniques, issues, and future research directions: a literature review-Reference-Cited by-同舟云学术

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Fake review detection techniques, issues, and future research directions: a literature review

Published:2024-05-17 Issue:9 Volume:66 Page:5071-5112
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Duma Ramadhani Ally,Niu Zhendong,Nyamawe Ally S.,Tchaye-Kondi Jude,Jingili Nuru,Yusuf Abdulganiyu Abdu,Deve Augustino Faustino

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10115-024-02118-2.pdf

Reference153 articles.

1. You L, Peng Q, Xiong Z, He D, Qiu M, Zhang X (2020) Integrating aspect analysis and local outlier factor for intelligent review spam detection. Futur Gener Comput Syst 102:163–172. https://doi.org/10.1016/j.future.2019.07.044

2. Khan ZY, et al (2021) A deep hybrid model for recommendation by jointly leveraging ratings, reviews and metadata information. Eng Appl Artif Intell 97:104066

3. Manaskasemsak B, Tantisuwankul J, Rungsawang A (2021) Fake review and reviewer detection through behavioral graph partitioning integrating deep neural network. Neural Comput Appl 1:1169–1182. https://doi.org/10.1007/s00521-021-05948-1

4. Vidanagama DU, Silva TP, Karunananda AS (2020) Deceptive consumer review detection: a survey. Artif Intell Rev 53(2):1323–1352. https://doi.org/10.1007/s10462-019-09697-5

5. Duma RA, et al (2024) DHMFRD–TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings. Multimedia Tools Appl 83(2):4533–4549

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1. A deep feature interaction and fusion model for fake review detection: Advocating heterogeneous graph convolutional network;Neurocomputing;2024-09

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