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.
|Title of host publication||Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings|
|Editors||Claudia Hauff, Joemon M. Jose, Dyaa Albakour, Ismail Sengor Altingovde, John Tait, Dawei Song, Stuart Watt|
|Number of pages||7|
|Publication status||Published - 2017|
|Event||39th European Conference on Information Retrieval, ECIR 2017 - Aberdeen, United Kingdom|
Duration: 2017 Apr 8 → 2017 Apr 13
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||39th European Conference on Information Retrieval, ECIR 2017|
|Period||17/4/8 → 17/4/13|
Bibliographical notePublisher Copyright:
© Springer International Publishing AG 2017.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)