First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.
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.
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.
To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source.
The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge.
The proliferation of fake news and its propagation on social media have become a major concern due to its ability to create devastating impacts.
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.
By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.