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Using Neural Network for Identifying Clickbaits in Online News Media

  • Amin OmidvarEmail author
  • Hui Jiang
  • Aijun An
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)

Abstract

Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017’s dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data analytics and visualization techniques are employed to explore and discover the provided dataset to get more insight from the data.

Keywords

Clickbait detection Text classification Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceYork UniversityTorontoCanada

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