Clickbait Detection

Abstract

Clickbait is a term that describes deceiving web content that uses ambiguity to provoke the user into clicking a link. It aims to increase the number of online readers in order to generate more advertising revenue. Clickbaits are heavily present on social media platforms wasting the time of users. We used supervised machine learning to create a model trained on 24 features extracted from a dataset of social media posts to classify the posts into two classes. This method achieved an F1-score of %79 and area under ROC curve of 0.7. The method used highlights the importance of using features extracted from different elements of a social media posts along with the traditional features extracted from the title and the article. In this research, we prove that it is possible to identify clickbaits using all parts of the post while having minimum number of features possible.