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Graph Learning for Fake Review Detection.

Shuo Yu1, Jing Ren2, Shihao Li1

  • 1School of Software, Dalian University of Technology, Dalian, China.

Frontiers in Artificial Intelligence
|July 7, 2022
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Summary
This summary is machine-generated.

This survey details graph learning methods for detecting fake reviews, crucial for combating misinformation on e-commerce and social media. It categorizes approaches and highlights challenges for future research in fake review detection.

Keywords:
anomaly detectiondata sciencefake review detectiongraph learningsocial computing

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Area of Science:

  • Data Science
  • Network Science
  • Computational Social Science

Background:

  • Fake reviews are prevalent on e-commerce and social media platforms.
  • Negative influence of fake reviews necessitates timely detection and response.
  • Graph learning methods offer a promising approach by integrating review attributes and relationships.

Purpose of the Study:

  • To conduct a detailed survey on fake review detection methods.
  • To present a comprehensive taxonomy of fake review detection, reviewer detection, and analysis.
  • To compare graph learning methods for fake review detection.

Main Methods:

  • Surveying advancements in fake review detection, reviewer detection, and analysis.
  • Summarizing different types of fake reviews and providing examples.
  • Discussing supervised and unsupervised graph learning approaches, including generation-based and contrast-based methods.

Main Results:

  • A comprehensive taxonomy of fake review detection is presented.
  • Graph learning methods, both supervised and unsupervised, are discussed.
  • Key challenges and open issues are identified.

Conclusions:

  • Effective fake review detection is a significant research area.
  • Graph learning methods are crucial for incorporating review relationships.
  • Future research should address data imperfections, explainability, and model efficiency.