Using Deep Networks and Transfer Learning to Address Disinformation
This addresses disinformation problems for social media and online platforms, but it is incremental as it combines existing methods.
The paper tackles disinformation detection by applying an ensemble pipeline of character-level CNN and LSTM with transfer learning, demonstrating effectiveness in tasks like spam emails and political sentiment.
We apply an ensemble pipeline composed of a character-level convolutional neural network (CNN) and a long short-term memory (LSTM) as a general tool for addressing a range of disinformation problems. We also demonstrate the ability to use this architecture to transfer knowledge from labeled data in one domain to related (supervised and unsupervised) tasks. Character-level neural networks and transfer learning are particularly valuable tools in the disinformation space because of the messy nature of social media, lack of labeled data, and the multi-channel tactics of influence campaigns. We demonstrate their effectiveness in several tasks relevant for detecting disinformation: spam emails, review bombing, political sentiment, and conversation clustering.