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CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter.

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Summary

This study introduces advanced deep learning models, Modified-LSTM and Modified GRU, to accurately detect fake news about COVID-19 on social media. The new models significantly outperform traditional machine learning methods in identifying false information.

Keywords:
COVID-19Fake newsdeep learningmisleading informationpandemicsocial media

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • The COVID-19 pandemic saw a parallel rise in misinformation, causing societal disruption and confusion.
  • Social media and the internet are primary sources of information, making them fertile ground for fake news dissemination.
  • Automated systems are crucial for detecting and mitigating the impact of false news.

Purpose of the Study:

  • To propose and evaluate an updated deep neural network framework for identifying fake news related to COVID-19.
  • To compare the performance of deep learning models against traditional machine learning algorithms for fake news detection.
  • To optimize deep learning model parameters using Keras-tuner for enhanced accuracy.

Main Methods:

  • Developed Modified Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning models (one to three layers).
  • Utilized a large dataset of COVID-19-related tweets, classifying claims as true or false.
  • Employed TF-IDF with N-gram for baseline machine learning models and word embeddings for deep learning models.
  • Optimized deep learning model parameters using Keras-tuner and validated on four benchmark datasets.

Main Results:

  • The proposed deep learning framework achieved high accuracy in detecting fake and non-fake tweets concerning COVID-19.
  • Demonstrated significant performance improvements compared to baseline machine learning models like Decision Trees, Logistic Regression, K Nearest Neighbors, Random Forests, Support Vector Machines, and Naive Bayes.
  • The Modified-LSTM and Modified GRU models proved effective in classifying COVID-19 information as fake or non-fake.

Conclusions:

  • The developed deep neural network approach offers a robust and accurate solution for combating COVID-19 misinformation.
  • Deep learning techniques, particularly Modified-LSTM and Modified GRU, show superior performance over traditional machine learning methods for fake news detection in this domain.
  • The study highlights the potential of advanced AI models in safeguarding public information during health crises.