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A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques.

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This study introduces advanced machine and deep learning models to detect fake news and rumors related to COVID-19. The BERT model demonstrated superior performance in identifying misinformation, offering a robust solution for content veracity analysis.

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Area of Science:

  • Computational Linguistics
  • Artificial Intelligence
  • Public Health Informatics

Background:

  • The COVID-19 pandemic has amplified the spread of misinformation, causing societal anxiety and confusion.
  • Rumors and fake news related to the virus propagate rapidly, fostering prejudice and fear.
  • Effective detection of COVID-19-related fake news is crucial for public discourse and well-being.

Purpose of the Study:

  • To propose and evaluate novel machine learning (ML) and deep learning (DL) models for detecting fake news.
  • To analyze the performance of various state-of-the-art models in identifying COVID-19 misinformation.
  • To determine the most effective model for real-world application in content veracity assessment.

Main Methods:

  • Utilized a COVID-19 rumors dataset comprising news articles and tweets.
  • Applied Natural Language Processing (NLP) and Deep Learning (DL) techniques for data analysis.
  • Evaluated models based on accuracy, precision, recall, and F1-score, including LSTM, Temporal Convolution Networks (TCN), and BERT.

Main Results:

  • Experiment 1: Temporal Convolution Networks (TCN) showed strong performance across veracity, stance, and sentiment metrics.
  • Experiment 2: The BERT model significantly outperformed other evaluated DL models (Simple RNN, LSTM variants, LSTM+CNN-1D) on all metrics.
  • The dataset included 9,200 Google comments and 34,779 filtered Twitter posts related to COVID-19 fake news.

Conclusions:

  • Deep learning models, particularly BERT, are highly effective in detecting fake news and rumors.
  • The study provides a validated approach for combating health misinformation using advanced AI.
  • Findings support the practical application of BERT for enhancing the reliability of online information.