Clickbait Detection using Multiple Categorization Techniques

• URL: http://arxiv.org/abs/2003.12961v1
• Date: Sun, 29 Mar 2020 07:16:41 GMT
• Title: Clickbait Detection using Multiple Categorization Techniques
• Abstract summary: This paper proposes a hybrid categorization technique for separating clickbait and non-clickbait articles. The proposed hybrid model is more robust, reliable and efficient than any individual categorization techniques.
• Score: 6.396288020763144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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