  Recent years have witnessed a widespread increase of rumor news generated by
humans and machines. Therefore, tools for investigating rumor news have become
an urgent necessity. One useful function of such tools is to see ways a
specific topic or event is represented by presenting different points of view
from multiple sources.
  In this paper, we propose Maester, a novel agreement-aware search framework
for investigating rumor news. Given an investigative question, Maester will
retrieve related articles to that question, assign and display top articles
from agree, disagree, and discuss categories to users. Splitting the results
into these three categories provides the user a holistic view towards the
investigative question. We build Maester based on the following two key
observations: (1) relatedness can commonly be determined by keywords and
entities occurring in both questions and articles, and (2) the level of
agreement between the investigative question and the related news article can
often be decided by a few key sentences. Accordingly, we use gradient boosting
tree models with keyword/entity matching features for relatedness detection,
and leverage recurrent neural network to infer the level of agreement. Our
experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order
of magnitude improvement of Maester over the original FNC winning solution, for
agreement-aware search.
