Detecting Clickbaits Using Deep Forest

News outlets employ catchy headlines that have the main purpose of luring the reader to click on the article and thus to visit the media site. These headlines are known as Clickbait. Although these baits may trick the readers into clicking, they often fail to deliver when it comes to the content of the article, leaving the reader disappointed. Furthermore, clickbaits can help the spread of misinformation, in the form of fake news, and have the power to influence people’s opinion.Our task is to automatically detect clickbaits from genuine article headlines. The developed system is based on Deep Forest, that is a non differentiable deep learning algorithm. As theoretical contribution, we extending the default Deep Forest implementation with a boosting mechanism. The Chakraborty dataset was used that comprises 32,000 headlines of news articles. Three approaches were used for feature engineering, namely word embeddings, headline extracted features and a combination of both.