This talk discusses what clickbaits are and how a machine learning model can be built using Python which is very effective in detecting clickbaits. The talk will start with very simple algorithms and go into advanced algorithms like RNNs. (The talk is very less mathematics and very much applied). Speaker shows that clickbaits can be detected with simple machine learning methods with 0.9 AUC.
Bait is something that is used to lure fishes. Clickbait is similar to that. It is used to lure humans to websites. However, studies have found that humans are more intelligent than fishes and more dangerous too.
Facebook started with detection of clickbaits in late 2014 and recently announced that it is going to reduce the number of clickbaits that appear in news feed (http://newsroom.fb.com/news/2016/08/news-feed-fyi-further-reducing-clickbait-in-feed/). With the penalization of clickbaits, it becomes important to check whether the content written by content writers consists of clickbait titles. In case it does, it will mean the content will be penalized and will appear in the top search results or facebook news feed very seldomly.
This talk gives an overview about what clickbaits are and how to recognize clickbaits using machine learning and python.