LSTM Powered Identification of Clickbait Content on Entertainment and News Websites

Abstract

Clickbait content on online platforms, is exaggerating content that doesn't deliver what it promises. The main motive of such content is to mislead the reader to “click” on them. These are widely responsible for delivering false information to the user and damaging their online experience. Many online creators deliberately use them to get more views and generate more revenue. In light of potential difficulties created by clickbait content, this paper aims to create a clickbait detection model for entertainment and news websites utilizing the power of the machine and deep learning models. Empirical results of our experiments indicate that LSTM models are best suited for identifying clickbait content containing text by achieving an accuracy of 95.031 % which is 1.138 times greater than the Random Forest and 1.709 times greater than the Naive Bayes model.