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Examining the Role of Clickbait Headlines to Engage Readers with Reliable Health-related Information [article]

Sima Bhowmik, Md Main Uddin Rony, Md Mahfuzul Haque, Kristen Alley Swain, Naeemul Hassan
2019 arXiv   pre-print
Clickbait headlines are frequently used to attract readers to read articles.  ...  After that, we would design an automation system to generate clickabit headlines to maximize user engagement.  ...  (Shu et al. 2018 ) used deep generative models to generate synthetic headlines with specific style labels and explored their utilities to help improve clickbait detection.  ... 
arXiv:1911.11214v1 fatcat:e2z5bbwwyrcmlho5uny7q6u4ky

Ensemble Learning Approach for Clickbait Detection Using Article Headline Features

2019 Informing Science  
Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines.  ...  Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait  ...  A deep generative variational autoencoder model was used for classification clickbaits (Zannettou, Chatzis, Papadamou, & Sirivianos, 2018) .  ... 
doi:10.28945/4279 fatcat:zpr2jyvuwzd3heqo4wqycxnmtm

Clickbait detection using word embeddings [article]

Vijayasaradhi Indurthi, Subba Reddy Oota
2017 arXiv   pre-print
Clickbait is a pejorative term describing web content that is aimed at generating online advertising revenue, especially at the expense of quality or accuracy, relying on sensationalist headlines or eye-catching  ...  We use distributed word representations of the words in the title as features to identify clickbaits in online news media.  ...  They rely on a rich set of 14 hand-crafted features to detect clickbait headlines.  ... 
arXiv:1710.02861v1 fatcat:czoopsgwg5gg7nw6q7enzlbn6m

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

Mohammed Al-Sarem, Faisal Saeed, Zeyad Ghaleb Al-Mekhlafi, Badiea Abdulkarem Mohammed, Mohammed Hadwan, Tawfik Al-Hadhrami, Mohammad T. Alshammari, Abdulrahman Alreshidi, Talal Sarheed Alshammari
2021 Applied Sciences  
Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose.  ...  Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language  ...  Acknowledgments: We would like to acknowledge the Scientific Research Deanship at the University of Ha'il, Saudi Arabia, for funding this research.  ... 
doi:10.3390/app11209487 fatcat:4c5ktcic2nfgxcojyy2ilzgt6a

Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects? [article]

Md Main Uddin Rony, Naeemul Hassan, Mohammad Yousuf
2017 arXiv   pre-print
detection model.  ...  The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays.  ...  features. [2] uses a dataset of 15, 000 manually labeled headlines to train several supervised models for clickbait detection.  ... 
arXiv:1703.09400v1 fatcat:ehrcjzbcujaqbmutuokyzlqt5e

The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube

Savvas Zannettou, Sotirios Chatzis, Kostantinos Papadamou, Michael Sirivianos
2018 2018 IEEE Security and Privacy Workshops (SPW)  
This problem, usually referred to as "clickbait," may severely undermine user experience. In this work, we study the clickbait problem on YouTube by collecting metadata for 206k videos.  ...  The use of deceptive techniques in user-generated video portals is ubiquitous.  ...  We also gratefully acknowledge the support of NVIDIA Corporation, with the donation of the Titan Xp GPU used for this research.  ... 
doi:10.1109/spw.2018.00018 dblp:conf/sp/ZannettouCPS18 fatcat:uiuy64zxundgtjtd5jshkbehbu

BaitBuster: A Clickbait Identification Framework

Md Main Uddin Rony, Naeemul Hassan, Mohammad Yousuf
The use of tempting and often misleading headlines (clickbait) to allure readers has become a growing practice nowadays among the media outlets.  ...  In this paper, we present BaitBuster, a browser extension and social bot based framework, that detects clickbaits floating on the web, provides brief explanation behind its decision, and regularly makes  ...  The Explanation and Summary Generator component detects if any of the n-grams is present in the requested headline.  ... 
doi:10.1609/aaai.v32i1.11378 fatcat:z4b6ohscwbfbtcdf33uicktvq4

Clickbait Detection of Indonesian News Headlines using Fine-Tune Bidirectional Encoder Representations from Transformers (BERT)

Diyah Utami Kusumaning Putri, Dinar Nugroho Pratomo
2022 Jurnal INFORM  
According to this problem, a clickbait detector is required to automatically identify news article headlines that include clickbait and non-clickbait.  ...  The evaluation results indicate that all fine-tuned IndoBERT classifiers outperform all word-vectors-based machine learning classifiers in classifying clickbait and non-clickbait Indonesian news headlines  ...  Agrawal proposed a deep learning model for clickbait detection of headlines using convolutional neural networks (CNN).  ... 
doi:10.25139/inform.v7i2.4686 fatcat:gdt33tjpvjcxrfjcfpksgjou4a

Using Neural Network for Identifying Clickbaits in Online News Media [article]

Amin Omidvar, Hui Jiang, Aijun An
2018 arXiv   pre-print
Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines.  ...  In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017's dataset.  ...  They extracted six novel features for clickbait detection and they showed in their results that these novel features are the most effective ones for detecting clickbait news headlines.  ... 
arXiv:1806.07713v1 fatcat:n3qmnyzba5fvzgirhaihpmqcte

Fake News Early Detection

Xinyi Zhou, Atishay Jain, Vir V. Phoha, Reza Zafarani
2020 Digital Threats: Research and Practice  
., information that is intentionally and verifiably false, or clickbaits [28], the headlines whose main purpose is to attract the attention of readers and encourage them to click on a link to a particular  ...  Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection.  ...  As few datasets, including PolitiFact and BuzzFeed, provide both news labels (fake or true) and news headline labels (clickbait or regular headline), we use a pretrained deep net; particularly, a Convolutional  ... 
doi:10.1145/3377478 fatcat:n2ja5ao6jfhczgrir4qv3h2qam

Clickbait Convolutional Neural Network

Hai-Tao Zheng, Jin-Yuan Chen, Xin Yao, Arun Sangaiah, Yong Jiang, Cong-Zhi Zhao
2018 Symmetry  
A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics  ...  Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines.  ...  However, their models are general classification models, which are not designed for clickbait detection.  ... 
doi:10.3390/sym10050138 fatcat:fwuolwcor5bv7ou5dtypddmqcq

Semi-Supervised Confidence Network aided Gated Attention based Recurrent Neural Network for Clickbait Detection [article]

Amrith Rajagopal Setlur
2018 arXiv   pre-print
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.  ...  detection.  ...  Same can't be said for the positive class ("obama", "iraq", "china", "president"). Hence we introduced a sigmoid layer to better suit the case of clickbait detection.  ... 
arXiv:1811.01355v1 fatcat:qsbfu7b7ife2djw6rw7nutqseu

Clickbait in YouTube Prevention, Detection and Analysis of the Bait using Ensemble Learning [article]

Peya Mowar, Mini Jain, Ruchika Goel, Dinesh Kumar Vishwakarma
2021 arXiv   pre-print
The developed clickbait detection model achieved a high accuracy of 92.89% for the novel BollyBAIT dataset and 95.38% for Misleading Video Dataset.  ...  , can be used to detect potential clickbait videos before they are uploaded, thereby preventing the nuisance of clickbaits altogether and improving the users streaming experience.  ...  Content-Agnostic Approaches for Clickbait Detection Zannetteou et al.  ... 
arXiv:2112.08611v1 fatcat:q5zrct6nazaa5pzvgdh2fceuqu

Experimental Evaluation of Clickbait Detection Using Machine Learning Models

Iftikhar Ahmad, Mohammed A. Alqarni, Abdulwahab Ali Almazroi, Abdullah Tariq
2020 Intelligent Automation and Soft Computing  
automated clickbait detection.  ...  This phenomenon is commonly known as clickbait. A number of machine learning techniques have been developed in the literature for automatic detection of clickbait.  ...  [2] proposed a model for stance detection in articles by comparing the headlines with body of the text.  ... 
doi:10.32604/iasc.2020.013861 fatcat:42gn2rl4dfglpoxmm25qghc2sy

We used Neural Networks to Detect Clickbaits: You won't believe what happened Next! [article]

Ankesh Anand, Tanmoy Chakraborty, Noseong Park
2019 arXiv   pre-print
Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits.  ...  Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge.  ...  [4] which has an even distribution of 7,500 clickbait headlines and 7,500 non-clickbait headlines.  ... 
arXiv:1612.01340v2 fatcat:iyeajua4czgkjjpr7lisstry5a
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