Clickbait Detection
9 papers with code • 0 benchmarks • 0 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)
Benchmarks
These leaderboards are used to track progress in Clickbait Detection
Latest papers
BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset
mdmotaharmahtab/banglabait
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We expect that this dataset and the detailed analysis and comparison of these clickbait detection models will provide a fundamental basis for future research into detecting clickbait titles in Bengali articles.
BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis
This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253, 070 data points collected through an automated process using the YouTube API and Python web automation frameworks.
A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles
Despite the importance of the task, to the best of our knowledge, there is no publicly available clickbait corpus for the Romanian language.
Clickbait Detection via Large Language Models
Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media.
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.
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.
Clickbait Detection in Tweets Using Self-attentive Network
zhouyiwei/cc
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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!
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
ankeshanand/deep-clickbait-detection
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Online content publishers often use catchy headlines for their articles in order to attract users to their websites.
