Strategies based on artificial intelligence for the detection of fake news

Abstract

The article examines artificial intelligence (AI) strategies to combat fake news, highlighting the rise in misinformation, especially during the pandemic, and its negative impact on public decision-making. The accelerated spread of fake news in the face of truth underlines the urgency of effective detection methods. Through a systematic literature review, the use of machine learning, deep learning, and natural language processing (NLP) to automate the identification of fake news is explored, highlighting key data sets such as BuzzFeedNews, LIAR, and BS Detector, among others, essential to train detection algorithms. The study discusses various AI approaches and techniques applied to detection, including convolutional neural networks (CNN), bidirectional LSTM, and the combination of CNN with LSTM, showing significant improvements in accuracy and efficiency. However, the limitations of these techniques are pointed out, such as the volatility of the training data and the difficulty of adapting models to different misinformation contexts. The conclusion highlights AI as a vital tool against fake news, emphasizing the need to advance research and develop more sophisticated technologies to strengthen disinformation detection and protect information integrity in society. The fight against fake news is complex, but AI-based strategies show a promising path toward practical solutions.