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Online Troll Reviewer Detection Using Deep Learning Techniques.

Mosleh Hmoud Al-Adhaileh1, Theyazn H H Aldhyani2, Ans D Alghamdi3

  • 1E-Learning and Distance Education, King Faisal University, Saudi Arabia, P.O. Box 4000 Al-Ahsa, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a CNN-BiLSTM model to detect troll reviewers on Reddit. The model achieved high accuracy in identifying troll posts using sentiment analysis and numerical data.

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Area of Science:

  • Computer Science
  • Social Media Analysis

Background:

  • Online discussions on social news aggregators like Reddit are susceptible to malicious actors known as trolls.
  • Identifying troll reviewers is crucial for maintaining the integrity of online discussions and link distribution.

Purpose of the Study:

  • To develop and evaluate a novel system for detecting troll reviewers in online discussions.
  • To differentiate troll reviewers from ordinary users based on their posting behavior and sentiment.

Main Methods:

  • Utilized a Convolutional Neural Network integrated with a Bidirectional Long Short-Term Memory (CNN-BiLSTM) model.
  • Employed sentiment analysis techniques, including machine learning and lexicon-based approaches, to analyze text data.
  • Extracted and analyzed numerical data (10 attributes) from a standard troll online reviewer dataset collected from Reddit.

Main Results:

  • The CNN-BiLSTM model achieved 97% accuracy when analyzing text data (sentiment analysis).
  • The model demonstrated 100% accuracy when analyzing numerical data.
  • The proposed model outperformed previously compared methods in troll reviewer detection.

Conclusions:

  • The CNN-BiLSTM model is highly effective for detecting troll reviewers on social media platforms like Reddit.
  • Sentiment analysis and numerical attribute extraction are valuable components for identifying malicious online behavior.
  • The developed system offers a robust solution for enhancing the safety and reliability of online discussion forums.