Sentiment as a ground truth stance indicator against fake news

This report investigates a novel ground truth approach to fake news detection, focusing on sentiment detection to interpret the author’s stance. A three-pronged model was built combining an LDA for topic detection, VADER to create sentiment sequence, a stacked LSTM to interpret those sentiment sequences and a statistical description of the sequences. The model interpreted the popular ISOT fake news dataset to build a knowledge base and achieved an 80% instance classification accuracy on the dataset using J48, finding value in sentiment analysis as a tool for fake news detection.