A Novel Approach for Clickbait Detection

Abstract

Clickbait refers to sensational headlines that often exaggerate facts, usually to entice readers to click on them. Many researchers have proposed different techniques involving various Machine Learning algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest, and Deep Learning techniques such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). Although there have been previous attempts by many researchers on detection of Clickbait titles, very few have taken into consideration the context of the title. Context plays a vital role in capturing the semantics of the text. Misclassification of Clickbait titles can be avoided using context. The Recurrent Convolutional Neural Network (RCNN) considers the context for text classification. In this system, clickbait classification is done using RCNN model, and later enhanced with LSTM and Gated Recurrent Unit (GRU) to capture long term dependencies and provide better accuracy than the previous state-of-the-art techniques.