Papers Clickbait Detection
“Clickbait Detection” 태그가 달린 논문 31편 · 필터 해제
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference
This paper focuses on detecting clickbait posts on the Web. These posts often use eye-catching disinformation in mixed modalities to mislead users to click for profit. That affects the user experience and thus would be b…
Causal InferenceClickbait DetectionPrompt-tuning for Clickbait Detection via Text Summarization
Clickbaits are surprising social posts or deceptive news headlines that attempt to lure users for more clicks, which have posted at unprecedented rates for more profit or commercial revenue. The spread of clickbait has s…
Clickbait DetectionSemantic SimilaritySemantic Textual SimilarityText SummarizationBanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset
Intentionally luring readers to click on a particular content by exploiting their curiosity defines a title as clickbait. Although several studies focused on detecting clickbait titles in English articles, low resource l…
ArticlesClickbait DetectionBaitBuster-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. The dataset co…
ClassificationClickbait DetectionMultilingual NLPA Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles
To increase revenue, news websites often resort to using deceptive news titles, luring users into clicking on the title and reading the full news. Clickbait detection is the task that aims to automatically detect this fo…
ArticlesClickbait DetectionContrastive LearningClickbait 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. Recently,…
Clickbait DetectionKnow Better – A Clickbait Resolving Challenge
In this paper, we present a new corpus of clickbait articles annotated by university students along with a corresponding shared task: clickbait articles use a headline or teaser that hides information from the reader to …
ArticlesClickbait DetectionQuestion AnsweringClickbait 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. This creates an incentive fo…
Clickbait DetectionClickbait 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. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using a hyperbolic and interesting w…
Clickbait DetectionLanguage ModelingLanguage ModellingSentence+2Clickbait Detection with Style-aware Title Modeling and Co-attention
Clickbait is a form of web content designed to attract attention and entice users to click on specific hyperlinks. The detection of clickbaits is an important task for online platforms to improve the quality of web conte…
Binary ClassificationClickbait DetectionClickbait Detection using Multiple Categorization Techniques
Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to …
ArticlesBIG-bench Machine LearningClickbait DetectionClustering+1Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly deviates from its title/thumbnail, has emerg…
Clickbait DetectionFederated 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. As the adverse effects of clickbait attract m…
ArticlesClickbait DetectionThe 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. Malicious content publishers misuse social media to manipulate as many users as possible to visit their websites using clic…
Clickbait DetectionregressionSemi-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. Such venal schemes thrive on exploiting the cur…
Clickbait DetectionGeneral ClassificationSWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection
In order to expand their reach and increase website ad revenue, media outlets have started using clickbait techniques to lure readers to click on articles on their digital platform. Having successfully enticed the user t…
ArticlesClickbait DetectionDocument EmbeddingFeature EngineeringCrowdsourcing a Large Corpus of Clickbait on Twitter
Clickbait has become a nuisance on social media. To address the urging task of clickbait detection, we constructed a new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017. To avoid biases in term…
Clickbait DetectionUsing Neural Network for Identifying Clickbaits in Online News Media
Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of…
Clickbait DetectionReinforced Co-Training
Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled data and select sa…
Clickbait DetectionGeneral ClassificationQ-Learningtext-classification+1Heuristic Feature Selection for Clickbait Detection
We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017. The challenge's task is to score the "clickbaitiness" of a given Twitter tweet on a scale from 0…
Clickbait DetectionDeep LearningFeature Engineeringfeature selection+1