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Fake news and misinformation have been increasingly used to manipulatepopular opinion and influence political processes. To better understand fakenews, how they are propagated, and how to counter their effect, it is necessaryto first identify them. Recently, approaches have been proposed toautomatically classify articles as fake based on their content. An importantchallenge for these approaches comes from the dynamic nature of news: as newpolitical events are covered, topics and discourse constantly change and thus,a classifier trained using content from articles published at a given time islikely to become ineffective in the future. To address this challenge, wepropose a topic-agnostic (TAG) classification strategy that uses linguistic andweb-markup features to identify fake news pages. We report experimental resultsusing multiple data sets which show that our approach attains high accuracy inthe identification of fake news, even as topics evolve over time.

A Topic-Agnostic Approach for Identifying Fake News Pages