  Online content publishers often use catchy headlines for their articles in
order to attract users to their websites. These headlines, popularly known as
clickbaits, exploit a user's curiosity gap and lure them to click on links that
often disappoint them. Existing methods for automatically detecting clickbaits
rely on heavy feature engineering and domain knowledge. Here, we introduce a
neural network architecture based on Recurrent Neural Networks for detecting
clickbaits. Our model relies on distributed word representations learned from a
large unannotated corpora, and character embeddings learned via Convolutional
Neural Networks. Experimental results on a dataset of news headlines show that
our model outperforms existing techniques for clickbait detection with an
accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.
