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Summary

This study introduces an explainable AI method for detecting deceptive online content using post metadata. It focuses on textual context, speaker background, and emotion, excluding subjective text analysis for more reliable fake news identification.

Keywords:
fake newsintentionally deceptive online contentmachine learning trained expert systemmessage characteristicssocial media

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Area of Science:

  • Artificial Intelligence
  • Computational Social Science
  • Information Science

Background:

  • Online content intentionally made deceptive poses a significant challenge.
  • Existing methods for detecting fake news often lack transparency or rely on subjective analysis.
  • There is a need for robust, explainable techniques to identify deceptive online information.

Purpose of the Study:

  • To develop and evaluate a post metadata-based approach for identifying intentionally deceptive online content.
  • To utilize an inherently explainable artificial intelligence (AI) technique for fake news detection.
  • To analyze the interrelationships and comparative importance of factors like textual context, speaker background, and emotion.

Main Methods:

  • An inherently explainable AI technique was employed, training an expert system using machine learning.
  • The study focused on post metadata, including textual context, speaker background, and emotion.
  • The efficacy of key factors was evaluated, deliberately excluding the subjective processing of post text itself.

Main Results:

  • The proposed method demonstrates the potential for a deceptive content detection tool.
  • Analysis revealed the interrelationships between factors used to determine deceptive content.
  • The comparative importance of different factors in identifying deceptive posts was assessed.

Conclusions:

  • A post metadata-based, explainable AI approach shows promise for identifying deceptive online content.
  • Focusing on objective factors like context, background, and emotion, rather than subjective text interpretation, enhances detection reliability.
  • This research contributes to understanding the complex network of factors influencing deceptive content and provides a foundation for future detection tools.