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Easier access to the internet and social media has made disseminatinginformation through online sources very easy. Sources like Facebook, Twitter,online news sites and personal blogs of self-proclaimed journalists have becomesignificant players in providing news content. The sheer amount of informationand the speed at which it is generated online makes it practically beyond thescope of human verification. There is, hence, a pressing need to developtechnologies that can assist humans with automatic fact-checking and reliableidentification of fake news. This paper summarizes the multiple approaches thatwere undertaken and the experiments that were carried out for the task.Credibility information and metadata associated with the news article have beenused for improved results. The experiments also show how modellingjustification or evidence can lead to improved results. Additionally, the useof visual features in addition to linguistic features is demonstrated. Adetailed comparison of the results showing that our models performsignificantly well when compared to robust baselines as well asstate-of-the-art models are presented.

Fake News Detection: Experiments and Approaches beyond Linguistic Features