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We apply an ensemble pipeline composed of a character-level convolutionalneural network (CNN) and a long short-term memory (LSTM) as a general tool foraddressing a range of disinformation problems. We also demonstrate the abilityto use this architecture to transfer knowledge from labeled data in one domainto related (supervised and unsupervised) tasks. Character-level neural networksand transfer learning are particularly valuable tools in the disinformationspace because of the messy nature of social media, lack of labeled data, andthe multi-channel tactics of influence campaigns. We demonstrate theireffectiveness in several tasks relevant for detecting disinformation: spamemails, review bombing, political sentiment, and conversation clustering.

Using Deep Networks and Transfer Learning to Address Disinformation