Clickbait Identification using Neural Networks

2017/10/24 by Philippe Thomas, Thomas, Philippe
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Misinformation and Its Impacts

paper · pdf · doi:10.48550/arxiv.1710.08721

openalex publication_date 2017/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

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

Related

• Using Neural Network for Identifying Clickbaits in Online News Media
• We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
• Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
• The Clickbait Challenge 2017: Towards a Regression Model for Clickbait\n Strength
• Clickbait detection using word embeddings