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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 1–31 of 31 papers

TitleStatusHype
BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait DatasetCode0
Clickbait Detection in Tweets Using Self-attentive NetworkCode0
A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News ArticlesCode0
A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based FeaturesCode0
BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal AnalysisCode0
Clickbait Detection via Large Language ModelsCode0
Using Neural Network for Identifying Clickbaits in Online News MediaCode0
We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!Code0
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!Code0
Federated Hierarchical Hybrid Networks for Clickbait Detection—0
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
The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength—0
Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach—0
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models—0
Clickbait Detection in YouTube Videos—0
Clickbait Detection using Multiple Categorization Techniques—0
Clickbait detection using word embeddings—0
Clickbait Detection with Style-aware Title Modeling and Co-attention—0
Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)—0
Clickbait Identification using Neural Networks—0
Crowdsourcing a Large Corpus of Clickbait on Twitter—0
Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?—0
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