Clickbait detection using multiple categorisation techniques

Abstract

Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempt visitors to click on a particular link either to monetise the landing page or to spread the false news for sensationalisation. The presence of clickbaits on any news aggregator portal may lead to unpleasant experience to readers. Automatic detection of clickbait headlines from news headlines has been a challenging issue for the machine learning community. A lot of methods have been proposed for preventing clickbait articles in recent past. However, the recent techniques available in detecting clickbaits are not much robust. This article proposes a hybrid categorisation technique for separating clickbait and non-clickbait articles by integrating different features, sentence structure and clustering. During preliminary categorisation, the headlines are separated using 11 features. After that, the headlines are recategorised using sentence formality and syntactic similarity measures. In the last phase, the headlines are again recategorised by applying clustering using word vector similarity based on t-stochastic neighbourhood embedding ( t-SNE) approach. After categorisation of these headlines, machine learning models are applied to the dataset to evaluate machine learning algorithms. The obtained experimental results indicate that the proposed hybrid model is more robust, reliable and efficient than any individual categorisation techniques for the dataset we have used.