  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 tempted visitors to click on a particular link either to monetize the
landing page or to spread the false news for sensationalization. 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 paper proposes a hybrid categorization technique for separating clickbait
and non-clickbait articles by integrating different features, sentence
structure, and clustering. During preliminary categorization, the headlines are
separated using eleven features. After that, the headlines are recategorized
using sentence formality, syntactic similarity measures. In the last phase, the
headlines are again recategorized by applying clustering using word vector
similarity based on t-Stochastic Neighbourhood Embedding (t-SNE) approach.
After categorization of these headlines, machine learning models are applied to
the data set to evaluate machine learning algorithms. The obtained experimental
results indicate the proposed hybrid model is more robust, reliable and
efficient than any individual categorization techniques for the real-world
dataset we used.
