  The rise of social media has enabled the widespread propagation of fake news,
text that is published with an intent to spread misinformation and sway
beliefs. Rapidly detecting fake news, especially as new events arise, is
important to prevent misinformation.
  While prior works have tackled this problem using supervised learning
systems, automatedly modeling the complexities of the social media landscape
that enables the spread of fake news is challenging. On the contrary, having
humans fact check all news is not scalable. Thus, in this paper, we propose to
approach this problem interactively, where humans can interact to help an
automated system learn a better social media representation quality. On real
world events, our experiments show performance improvements in detecting
factuality of news sources, even after few human interactions.
