Clickbaits, in social media, are exaggerated headlines whose main motive is to mislead the reader to “click” on them. They create a nuisance in the online experience by creating a lure towards poor content. Online content creators are utilizing more of them to get increased page views and thereby more ad revenue without providing the backing content. This paper proposes a model for detection of clickbait by utilizing convolutional neural networks and presents a compiled clickbait corpus. We create a corpus using multiple social media platforms and utilize deep learning for learning features rather than undergoing the long and complex process of feature engineering. Our model achieves high performance in identification of clickbaits.