We also demonstrate the the detection of inflammatory, inauthentic, or otherwise ability to use this architecture to transfer knowledge nefarious communication. Character-level convolutional from labeled data in one domain to related neural networks (CNNs) are particularly well-suited for (supervised and unsupervised) tasks. Characterlevel this task--as opposed to a word-level model--because they neural networks and transfer learning are allow for non-vernacular discourse, misspelling, and other particularly valuable tools in the disinformation social media features (e.g., emoticons) to be learned without space because of the messy nature of social media, the constraint of fixed vocabularies (Zhang et al., 2015). We lack of labeled data, and the multi-channel tactics implement an adaptation of a neural network architecture of influence campaigns. We demonstrate their effectiveness recently demonstrated to be effective for text classification in several tasks relevant for detecting (Zhang et al., 2015; Józefowicz et al., 2016). The method disinformation: spam emails, review bombing, is purely content-based and does not require any additional political sentiment, and conversation clustering.