  Consuming news from social media is becoming increasingly popular. However,
social media also enables the widespread of fake news. Because of its
detrimental effects brought by social media, fake news detection has attracted
increasing attention. However, the performance of detecting fake news only from
news content is generally limited as fake news pieces are written to mimic true
news. In the real world, news pieces spread through propagation networks on
social media. The news propagation networks usually involve multi-levels. In
this paper, we study the challenging problem of investigating and exploiting
news hierarchical propagation network on social media for fake news detection.
  In an attempt to understand the correlations between news propagation
networks and fake news, first, we build a hierarchical propagation network from
macro-level and micro-level of fake news and true news; second, we perform a
comparative analysis of the propagation network features of linguistic,
structural and temporal perspectives between fake and real news, which
demonstrates the potential of utilizing these features to detect fake news;
third, we show the effectiveness of these propagation network features for fake
news detection. We further validate the effectiveness of these features from
feature important analysis. Altogether, this work presents a data-driven view
of hierarchical propagation network and fake news and paves the way towards a
healthier online news ecosystem.
