Clickbait Detection Edit Task
Natural Language Processing
About
Clickbait detection is the task of identifying clickbait, a form of false advertisement, that uses hyperlink text or a thumbnail link that is designed to attract attention and to entice users to follow that link and read, view, or listen to the linked piece of online content, with a defining characteristic of being deceptive, typically sensationalized or misleading (Source: Adapted from Wikipedia)
Benchmarks
Greatest papers with code
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
• ankeshanand/deep-clickbait-detection
• 
Online content publishers often use catchy headlines for their articles in order to attract users to their websites.
Clickbait Detection in Tweets Using Self-attentive Network
• zhouyiwei/cc
•
Clickbait detection in tweets remains an elusive challenge.
We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!
And we have totally tested it, trust us!
A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge.
Using Neural Network for Identifying Clickbaits in Online News Media
Online news media sometimes use misleading headlines to lure users to open the news article.