natural-language-processing-10
Clickbait Detection
3260 papers • 126 benchmarks • 313 datasets
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)
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Most implemented papers
We Used Neural Networks to Detect Clickbaits: You Won't Believe What Happened Next!
Tanmoy Chakraborty, Ankesh Anand, Noseong Park•Sun Dec 04 2016
A neural network architecture based on Recurrent Neural Networks for detecting clickbaits is introduced, which relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks.
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