Truth or Lie: Pre-emptive Detection of Fake News in Different Languages Through Entropy-based Active Learning and Multi-model Neural Ensemble

In recent times, the circulation of fake news on social networks has increased exponentially with spikes in propagation seen during and after the 2016 US elections. Hence, there has been a surge in research into automated fake news detection. However, most research tends towards supervised learning which requires a significant amount of labeled data which is difficult to obtain. Thus, in this paper, we develop a semi-supervised learning method for fake news detection incorporating active learning based on entropy as a query strategy to train a multi-model neural ensemble architecture. The goal of the research is to achieve high accuracy on fake news detection while using lower amounts of data. Our experiments against other standards indicate promising results, with our model achieving high accuracy with 4% to 28% of the dataset.