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A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in

Nureni Ayofe Azeez1, Sanjay Misra2, Davidson Onyinye Ogaraku1

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This study uses supervised AI algorithms to classify fake news on social media. Machine learning models accurately identify false information, enhancing online content integrity.

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Area of Science:

  • Computer Science
  • Information Science

Background:

  • The proliferation of fake news on social media poses a significant threat to societal trust and democratic institutions.
  • Effective detection of misinformation is crucial for maintaining information integrity.

Purpose of the Study:

  • To investigate the effectiveness of supervised AI algorithms for classifying fake news.
  • To identify optimal machine learning models for fake news detection across diverse datasets.

Main Methods:

  • Utilized supervised AI algorithms including Passive Aggressive Classifier, perceptron, and decision stump.
  • Trained 29 models on various social media datasets, employing TF-IDF and Count Vectorizers for feature generation.
  • Employed sensors for data collection and rigorous data preprocessing techniques.

Main Results:

  • Evaluated model performance using accuracy, precision, and recall metrics.
  • Identified best-performing algorithms for each dataset: SG (Dataset 1), BernoulliRBM (Dataset 2), LinearSVC (Dataset 3), and BernoulliRBM (Dataset 4).
  • Demonstrated the potential of AI in distinguishing genuine news from fabricated content.

Conclusions:

  • Supervised AI algorithms offer a viable solution for combating fake news and preserving information integrity.
  • The findings have significant implications for academia and practical applications in safeguarding democratic discourse.
  • Highlights the role of sensors and big data analytics in information integrity within IoT and smart city contexts.