Clickbait Detection Using Long short-term memory

The exploitation of clickbait has lately risen on many social media sites. Click bait is catchy titles or headlines with the primary goal of attracting attention and encouraging visitors to "click" on a headline. Media Clickbait is widely utilized, and detecting it is a critical step. This research presents a technique for detecting clickbait headlines on social media that employs a deep learning algorithm, especially a form of Recurrent Neural Network known as Long short-term memory. The method used focuses on textual characteristics, takes word sequence context into account, and derives colloquial expressions from the complete dataset. The headlines are vectorized using Word2vec Word embedding. Our conclusions were quite accurate, with a 96 percent accuracy rate, this is significantly more than conventional Machine Learning algorithms. A comparison utilizing the Naive Bayes classifier, a classification technique, was also performed.