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  5. Information And Computing Sciences
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  9. Knowledge Representation And Reasoning
  11. Ensemble Learning Approach For Distinguishing Human And Computer-generated Arabic Reviews.

Ensemble learning approach for distinguishing human and computer-generated Arabic reviews.

Fatimah Alhayan1, Hanen Himdi2

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

Peerj. Computer Science
|December 9, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study differentiates human and AI-generated Arabic reviews using machine learning. Computer-generated reviews show distinct linguistic patterns, aiding in fake review detection and boosting consumer trust.

Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Customer reviews are vital for businesses, but AI-generated fake reviews erode consumer trust.
  • Existing research on detecting AI-generated text primarily focuses on English, with a gap in Arabic.
  • Ensemble learning (EL) techniques for classifying Arabic fake reviews are underexplored.

Purpose of the Study:

  • To develop and evaluate models for classifying human versus computer-generated Arabic reviews.
  • To investigate the effectiveness of ensemble learning, specifically soft voting, in this classification task.
  • To identify linguistic features that differentiate human and AI-generated Arabic reviews.

Main Methods:

  • Employed traditional machine learning, deep learning, and transformer models.
Keywords:
Computer-generated reviews detectionDeep learningEnsemble learningMachine learning

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  • Utilized ensemble techniques, including soft voting, combining logistic regression (LR) and convolutional neural network (CNN) models.
  • Conducted textual analysis focusing on parts of speech (POS), emotions, and linguistic patterns.

    • Main Results:

    • Achieved high accuracy in classification, with an ensemble of LR and CNN reaching 89.70%, comparable to AraBERT's 90.0%.
    • Identified significant linguistic disparities: AI reviews contained a much higher proportion of adjectives (6.3%) than human reviews (0.46%).
    • Demonstrated the effectiveness of ensemble methods in improving fake review detection.

    Conclusions:

    • The study successfully differentiates human and AI-generated Arabic reviews, offering a valuable tool for businesses.
    • Linguistic analysis provides key insights into the characteristics of fake reviews, aiding detection efforts.
    • Advances Arabic Natural Language Processing (NLP) and provides practical implications for maintaining marketplace integrity and consumer trust.
    Textual analysis