Automatic Differentiation Between Legitimate and Fake News Using Named Entity Recognition

Today, the increasing ease of publishing information online combined with a gradual shift of paradigm from consuming news via conventional media to non-conventional media calls for a computational and automatic approach to the identification of an article's legitimacy. In this study, we propose an approach for cross-domain fake news detection focusing on the identification of legitimate content from a pool of articles that are of varying degrees of legitimacy. We present a model as a proof of concept as well as data gathered from evaluating the model on Fake-News AMT, a dataset released for cross-domain fake news detection. The results of our model are then compared against a baseline model which has served as the benchmark for the dataset. We find all results in support of our hypothesis. Our proof-of-concept model has also outperformed the benchmark in the domains Technology and Entertainment as well as when it was run on the whole dataset at once.