Detecting Fake News Articles

Abstract

Fake news has been generated and widely spread although journalists and researchers created fact-checking websites (e.g., Snopes and PolitiFact) and analyzed characteristics of fake news. To fill this gap, in this paper we focus on developing machine learning models based on only text information in news articles toward automatically detecting fake news. In particular, we proposed a framework which extracts 134 features and builds traditional known machine learning models like Random Forest and XGBoost. We also propose a deep learning based model (LSTM with self-attention mechanism) to see which one performs better in the fake news article detection in both political news and celebrity news domains. In the experiments, we compare our models against 7 baselines. The results show that our XGBoost model improved 16.4% and 13.1% over the best baseline in terms of accuracy in both political news articles and celebrity news articles, respectively.