Clickbait Identification using Neural Networks

Abstract

This paper presents the results of our participation in the Clickbait Detection Challenge 2017. The system relies on a fusion of neural networks, incorporating different types of available informations. It does not require any linguistic preprocessing, and hence generalizes more easily to new domains and languages. The final combined model achieves a mean squared error of 0.0428, an accuracy of 0.826, and a F1 score of 0.564. According to the official evaluation metric the system ranked 6th of the 13 participating teams.

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Residual Learning for Image Recognition
• Entity Embeddings of Categorical Variables
• The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength

Cited by in corpus (5)

• Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks
• BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset
• Using Neural Network for Identifying Clickbaits in Online News Media
• Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
• Is it a click bait? Let's predict using Machine Learning