We used neural networks to detect clickbaits: You won’t believe what happened next!

Ankesh Anand, Tanmoy Chakraborty, Noseong Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

47 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings
EditorsClaudia Hauff, Joemon M. Jose, Dyaa Albakour, Ismail Sengor Altingovde, John Tait, Dawei Song, Stuart Watt
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783319566078
Publication statusPublished - 2017
Event39th European Conference on Information Retrieval, ECIR 2017 - Aberdeen, United Kingdom
Duration: 2017 Apr 82017 Apr 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10193 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference39th European Conference on Information Retrieval, ECIR 2017
Country/TerritoryUnited Kingdom
City Aberdeen

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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