FacTweet: Profiling Fake News Twitter Accounts
This addresses the problem of fake news spread on social media for platforms and users, but it is incremental as it builds on existing detection methods with a modified approach.
The paper tackles fake news detection on Twitter by profiling accounts using a neural recurrent model that processes tweet timelines as chunks rather than individual tweets, achieving experimental benefits over strong baselines.
We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently. We show the experimental benefits of modeling latent stylistic signatures of mixed fake and real news with a sequential model over a wide range of strong baselines.