Identification of clickbait news articles using SBERT and correlation matrix

Abstract

Clickbait refers to the practice of using attention-grabbing or misleading headlines to attract readers to click on particular headlines or pieces of content. This technique often involves exaggerating claims or adding false information to attract traffic. In general, clickbait headlines are less similar to detailed articles associated with that headline compared to the standard news post. In our proposed system, we have exploited that to create features from headlines and its paragraph by using SBERT. Since the size of the paragraph is quite large compared to the headlines, we have selected a few sentences from the paragraph using a dissimilarity matrix. The extracted features from the headlines, target title and selected sentences of the paragraph are concatenated and classified using machine learning (ML) classifiers. The proposed model was tested extensively on two real-world datasets, and the results showed that it performed better than the current state-of-the-art models. The experimental results, the support vector machine (SVM) classifier with the concatenated embedding of six dissimilar sentences from paragraph exhibited the best performance with an accuracy of 0.84, weighted precision of 0.83, weighted recall of 0.84, and weighted F1-score of 0.82 surpassing the state-of-the-art model by 0.088 in terms of F1-score.