  Easier access to the internet and social media has made disseminating
information through online sources very easy. Sources like Facebook, Twitter,
online news sites and personal blogs of self-proclaimed journalists have become
significant players in providing news content. The sheer amount of information
and the speed at which it is generated online makes it practically beyond the
scope of human verification. There is, hence, a pressing need to develop
technologies that can assist humans with automatic fact-checking and reliable
identification of fake news. This paper summarizes the multiple approaches that
were undertaken and the experiments that were carried out for the task.
Credibility information and metadata associated with the news article have been
used for improved results. The experiments also show how modelling
justification or evidence can lead to improved results. Additionally, the use
of visual features in addition to linguistic features is demonstrated. A
detailed comparison of the results showing that our models perform
significantly well when compared to robust baselines as well as
state-of-the-art models are presented.
