Fact or Fake? How News Title, Sentiment and Writing Style help AI to detect COVID-19 Fake News? - Chen-Shu Wang; Bo-Yi Li; Kai-Wen Wang; Zhi-Chi Lin (Applied Artificial Intelligence)
This research presents a sophisticated model aimed at detecting COVID-19 related misinformation in Traditional Chinese, a critical response to the swift spread of fake news during the pandemic. The model employs an ensemble model of machine learning techniques, such as SVM, LSTM, BiLSTM, and BERT, along with a diverse array of input features including news structure, sentiment, and writing stylistic elements. Testing of the model has shown an impressive 97% accuracy in differentiating factual from fraudulent news. A significant finding is that in-depth content analysis offers more insights compared to mere headline scrutiny, though headlines do aid in marginally increasing accuracy. The integration of sentiment analysis and stylistic nuances further boosts the model’s effectiveness. This study is pivotal in establishing a robust Traditional-Chinese fake news detection mechanism for COVID-19, underscoring the effectiveness of combined machine learning strategies for more consistent and reliable outcomes.