Fact or Fiction

Subjective reporting polarizes competing viewpoints. However, helping readers to recognize subjective content leads to more impartial discussions. Towards this end, we develop machine learning models that classify sentence objectivity. We contribute a set of linguistic rules for determining sentence objectivity collated from previous work. We also develop a labeled dataset with over 5000 sentences retrieved from various news sources. Further, we evaluate traditional machine learning classification models and artificial neural networks on our dataset. The best performing model, a convolutional neural network, achieved an accuracy of 85% and an AUC of 0.933. Using our subjective-objective sentence classification model, we implement Fact-or-Fiction, an end-to-end web system that highlights objective sentences in user text. Fact-or-Fiction provides additional information, such as links to related web pages and related previous submissions.