We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!

Code

• github.com/khelloufelbakrilaaroussi/El-bekri-Laaroussi-Khellouf
  tf★ 0
• github.com/ankeshanand/deep-clickbait-detection
  tf★ 0

Abstract

Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user's curiosity gap and lure them to click on links that often disappoint them. Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge. Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits. Our model relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks. Experimental results on a dataset of news headlines show that our model outperforms existing techniques for clickbait detection with an accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.

Tasks

Reproductions