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Clickbait Detection

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)

Papers

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Showing 11–20 of 31 papers

TitleStatusHype
From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles—0
Heuristic Feature Selection for Clickbait Detection—0
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks—0
Know Better – A Clickbait Resolving Challenge—0
Machine Learning Based Detection of Clickbait Posts in Social Media—0
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference—0
Prompt-tuning for Clickbait Detection via Text Summarization—0
Reinforced Co-Training—0
Semi-Supervised Confidence Network aided Gated Attention based Recurrent Neural Network for Clickbait Detection—0
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection—0
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