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 with no code
Prompt-tuning for Clickbait Detection via Text Summarization
To address this problem, we propose a prompt-tuning method for clickbait detection via text summarization in this paper, text summarization is introduced to summarize the contents, and clickbait detection is performed based on the similarity between the generated summary and the contents.
Clickbait Detection in YouTube Videos
YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video.
Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)
Click counts are related to the amount of money that online advertisers paid to news sites.
Clickbait Detection using Multiple Categorization Techniques
The obtained experimental results indicate the proposed hybrid model is more robust, reliable and efficient than any individual categorization techniques for the real-world dataset we used.
Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos.
Federated Hierarchical Hybrid Networks for Clickbait Detection
Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization.
The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
Clickbait has grown to become a nuisance to social media users and social media operators alike.
Semi-Supervised Confidence Network aided Gated Attention based Recurrent Neural Network for Clickbait Detection
Clickbaits are catchy headlines that are frequently used by social media outlets in order to allure its viewers into clicking them and thus leading them to dubious content.
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection
We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture.
Crowdsourcing a Large Corpus of Clickbait on Twitter
To address the urging task of clickbait detection, we constructed a new corpus of 38, 517 annotated Twitter tweets, the Webis Clickbait Corpus 2017.
