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Detecting opinion spams through supervised boosting approach.

Mohamad Hazim1, Nor Badrul Anuar1, Mohd Faizal Ab Razak1,2

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This study introduces statistical features and supervised boosting models like XGBoost and GBM to detect opinion spam in mobile app reviews. XGBoost excelled in English, GBM Gaussian in Malay, achieving high accuracy for spam detection.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Mining

Background:

  • Product reviews are crucial for businesses and consumers, but opinion spam disrupts trust and accuracy in platforms like Google Playstore.
  • Existing spam detection methods, often using time series and neural networks, are computationally intensive and limited by feature granularity.
  • The need for more accurate and efficient opinion spam detection is evident, especially in multilingual mobile application marketplaces.

Purpose of the Study:

  • To propose and evaluate statistical-based features for improved opinion spam detection in mobile application reviews.
  • To compare the effectiveness of supervised boosting models, specifically Extreme Gradient Boost (XGBoost) and Generalized Boosted Regression Model (GBM), for spam detection.
  • To assess model performance on multilingual datasets (English and Malay) to understand language-specific efficacy.

Main Methods:

  • Utilized statistical features modeled through supervised boosting algorithms: Extreme Gradient Boost (XGBoost) and Generalized Boosted Regression Model (GBM).
  • Evaluated the proposed methods on two multilingual datasets, comprising English and Malay mobile application reviews.
  • Conducted comparative analysis to determine the most suitable model for each language dataset.

Main Results:

  • XGBoost demonstrated superior performance for opinion spam detection in the English dataset.
  • GBM Gaussian proved most effective for detecting opinion spam within the Malay dataset.
  • The proposed statistical features achieved high detection accuracy rates: 87.43% for English and 86.13% for Malay.

Conclusions:

  • Statistical features combined with supervised boosting models offer a robust approach to opinion spam detection.
  • Model selection is crucial, with XGBoost and GBM Gaussian showing language-specific suitability.
  • The study successfully improved spam detection accuracy in multilingual mobile app review contexts.