Cross-Domain Learning forClassifying Propaganda in Online Contents
As news and social media exhibit an increasing amount of manipulative polarized content, detecting such propaganda has received attention as a new task for content analysis. Prior work has focused on supervised learning with training data from the same domain...
However, as propaganda can be subtle and keeps evolving, manual identification and proper labeling are very demanding. As a consequence, training data is a major bottleneck. In this paper, we tackle this bottleneck and present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches