Abstract
Clickbait, which is a technique for creating an attractive title, is a way to generate income by increasing reader and visitor traffic. The factor of the rise of clickbait in online media is the increasingly fierce competition between media to get readers or visitors to the exclusion of site visitor satisfaction using clickbait. Based on this problem, a classifier is needed to detect news that is clickbait or not clickbait. Recent research on clickbait uses M-BERT by focusing news analysis on the news headlines. But clickbait is easier to identify by assessing the similarity of meaning between the headline and the news content. It uses a combination of siamese network architecture. This study proposes a siamese network in determining the similarity between the title and scope of the news to determine whether the news is clickbait news. The combination of siamese and ColBERT networks to produce the given embedding sentences and have text similarity values can perform better than the baseline system.
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