The proliferation of fake news and its propagation on social media have become a major concern due to its ability to create devastating impacts.
Over the past few years, we have been witnessing the rise of misinformation on the Web.
Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured.
One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.
Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume.
To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source.
By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.