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A comprehensive Benchmark for fake news detection.

Antonio Galli1, Elio Masciari1, Vincenzo Moscato1

  • 1Department of Electrical and Information Technology (DIETI), University of Naples, Federico II via Claudio 21, 80125 Naples, Italy.

Journal of Intelligent Information Systems
|March 28, 2022
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This study benchmarks machine and deep learning for fake news detection, crucial for combating misinformation on social media. It analyzes various techniques and feature combinations, highlighting their performance trade-offs.

Keywords:
BenchmarkingDeep learningFake news detection

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • The proliferation of fake news on social media poses significant threats to democracy and public trust.
  • Developing accurate fake news detection strategies is critical, especially given limited information on news propagation.

Discussion:

  • This research provides a benchmark framework to analyze and compare prominent machine learning and deep learning techniques for fake news detection.
  • The study explores various feature combinations beyond those typically found in existing literature.

Key Insights:

  • Experiments on real-world datasets reveal the distinct advantages and disadvantages of different fake news detection approaches regarding accuracy and efficiency.
  • The effectiveness of these methods is evaluated even when content information is limited.

Outlook:

  • Further research can refine these models for improved fake news detection performance.
  • Developing robust detection systems is essential for mitigating the societal impact of misinformation.