Tl;dr: No. Wrong output format, wrong training set, wrong input. To create a classifier that does that, you'd need a labeled set - i.e. someone would have to g

Tl;dr: No. Wrong output format, wrong training set, wrong input. To create a classifier that does that, you'd need a labeled set - i.e. someone would have to go through and say "this headline is 3 clickbaits. This other headline is 8 clickbaits". You could also sort between clickbaity and non-clickbaity, but that would still require manual work. You could get that programatically through a few different means, but you'd need a lot more than just headlines. It also probably wouldn't be a good idea to use a RNN - it doesn't suit the data format well. It'd be better to use a neural network (non-recurrent) or logistic regression with the entire headline as input. Fortunately, it'll converge on a good solution a LOT faster - fewer parameters to tune + simpler output = fewer examples needed to figure out what's going on - so you might be able to get something that has plausible levels of accuracy with a day or two of set labeling (estimate brought to you by my ass).