{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/we-used-neural-networks-to-detect-clickbaits","title":"We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!","arxiv_id":"1612.01340","date":"2016-12-05","proceeding":null,"authors":["Ankesh Anand","Tanmoy Chakraborty","Noseong Park"],"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.","url_abs":"https://arxiv.org/abs/1612.01340v2","url_pdf":"https://arxiv.org/pdf/1612.01340v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"we-used-neural-networks-to-detect-clickbaits","repo_url":"https://github.com/ankeshanand/deep-clickbait-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"we-used-neural-networks-to-detect-clickbaits","repo_url":"https://github.com/khelloufelbakrilaaroussi/El-bekri-Laaroussi-Khellouf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"clickbait-detection","task_name":"Clickbait Detection"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1612.01340","atlas_url":"https://app.syntology.ai/?focus=1612.01340","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}