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Related Concept Videos

Understanding Deception01:14

Understanding Deception

Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...

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Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce

Saleh Nagi Alsubari1, Theyazn H H Aldhyani2, Sachin N Deshmukh1

  • 1Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India.

Computational Intelligence and Neuroscience
|November 28, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a deceptive review detection system using a recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model. The system achieved 89.6% accuracy in identifying fake reviews on Amazon and Yelp datasets, outperforming existing methods.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • E-commerce Analytics

Background:

  • Online reviews significantly influence consumer purchasing decisions.
  • Deceptive reviews mislead consumers and harm e-commerce platforms.
  • Fraudsters create fake reviews for personal gain or to damage competitors.

Purpose of the Study:

  • To develop and evaluate a system for detecting deceptive product reviews.
  • To analyze deceptive reviews in the Amazon and Yelp e-commerce domains.
  • To compare the performance of a recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model against existing methods.

Main Methods:

  • Utilized Linguistic Inquiry and Word Count (LIWC) tool for feature extraction from review text.
  • Engineered a deceptiveness score based on features like authenticity, sentiment, and analytical thinking.
  • Applied a recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model trained on word embeddings for classification.

Main Results:

  • The RNN-BLSTM model achieved a testing accuracy of 89.6% on both Yelp and Amazon datasets.
  • The integration of LIWC features with word embeddings demonstrated superior performance.
  • The proposed method effectively identified and classified deceptive online reviews.

Conclusions:

  • The developed deceptive review detection system is effective and accurate.
  • LIWC features combined with word embeddings offer a robust approach for detecting fake reviews.
  • This research contributes to enhancing consumer trust and e-commerce integrity.