We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
arXiv:1612.01340
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.
Accepted to the European Conference on Information Retrieval (ECIR), 2017
References in corpus (1)
Cited by in corpus (5)
- Learning to Identify Ambiguous and Misleading News Headlines
- A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
- Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?
- Clickbait detection using word embeddings
- Towards Understanding the Information Ecosystem Through the Lens of Multiple Web Communities