A Comparative Approach For Clickbait Detection Using Deep Learning

The use of Online Clickbait in different social media platforms have increased momentarily. Basically, click baits are the eye-catching titles or headlines which exaggerate the facts and make the user to “click” on it. These clickbaits comes in many forms like images, videos also through advertisements. This links will lead you to anonymous websites which contains very little information and create nuisance on the internet. In social media clickbaits are very commonly used and Detection of Clickbait is a very crucial process. This paper proposes a method using a deep learning algorithm namely Convolution Neural Network (CNN) for detecting the clickbaits on the social media platforms. The used method focuses on the textual features which consider the word sequence information and also learns the word meanings from entire dataset. Our Results obtained a high accuracy of 0.82% comparatively better than different Machine Learning algorithms. We also did comparative analysis with the classification algorithm called Random Forest (RF).