  Stopping the malicious spread and production of false and misleading news has
become a top priority for researchers. Due to this prevalence, many automated
methods for detecting low quality information have been introduced. The
majority of these methods have used article-level features, such as their
writing style, to detect veracity. While writing style models have been shown
to work well in lab-settings, there are concerns of generalizability and
robustness. In this paper, we begin to address these concerns by proposing a
novel and robust news veracity detection model that uses the content sharing
behavior of news sources formulated as a network. We represent these content
sharing networks (CSN) using a deep walk based method for embedding graphs that
accounts for similarity in both the network space and the article text space.
We show that state of the art writing style and CSN features make diverse
mistakes when predicting, meaning that they both play different roles in the
classification task. Moreover, we show that the addition of CSN features
increases the accuracy of writing style models, boosting accuracy as much as
14\% when using Random Forests. Similarly, we show that the combination of
hand-crafted article-level features and CSN features is robust to concept
drift, performing consistently well over a 10-month time frame.
