Tl;dr: No. Wrong output format, wrong training set, wrong input.<p>To create a classifier that does that, you&#x27;d need a labeled set - i.e. someone would have to go through and say &quot;this headline is 3 clickbaits. This other headline is 8 clickbaits&quot;. You could also sort between clickbaity and non-clickbaity, but that would still require manual work.<p>You could get that programatically through a few different means, but you&#x27;d need a lot more than just headlines.<p>It also probably wouldn&#x27;t be a good idea to use a RNN - it doesn&#x27;t suit the data format well. It&#x27;d be better to use a neural network (non-recurrent) or logistic regression with the entire headline as input.<p>Fortunately, it&#x27;ll converge on a good solution a LOT faster - fewer parameters to tune + simpler output = fewer examples needed to figure out what&#x27;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).