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Identifying Clickbait

Vaibhav Kumar, Dhruv Khattar, Siddhartha Gairola, Yash Kumar Lal, Vasudeva Varma
2018 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18  
Traditional methods for clickbait detection have relied heavily on feature engineering which, in turn, is dependent on the dataset it is built for.  ...  Online media outlets, in a bid to expand their reach and subsequently increase revenue through ad monetisation, have begun adopting clickbait techniques to lure readers to click on articles.  ...  We used a batch size of 256 and used adadelta [16] as a gradient based optimizer for learning the parameters of the model.  ... 
doi:10.1145/3209978.3210144 dblp:conf/sigir/KumarKGLV18 fatcat:hzrotbb5ybdnhox24wtq5zuqti

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 new deep learning and metric learning based hybrid techniques integrated with a case based reasoning methodology are proposed for adaptable clickbait detection (López-Sánchez, Herrero, Arrieta, & Corchado  ... 
doi:10.28945/4279 fatcat:zpr2jyvuwzd3heqo4wqycxnmtm

Clickbait Identification using Neural Networks [article]

Philippe Thomas
2017 arXiv   pre-print
The final combined model achieves a mean squared error of 0.0428, an accuracy of 0.826, and a F1 score of 0.564.  ...  This paper presents the results of our participation in the Clickbait Detection Challenge 2017.  ...  Ministry of Education and Research (BMBF) through the projects ALL SIDES (01IW14002) and BBDC (01IS14013E).  ... 
arXiv:1710.08721v1 fatcat:7f6dj2hsqndqleh4fluiiruj4q

Clickbait Detection Using Deep Recurrent Neural Network

Abdul Razaque, Bandar Alotaibi, Munif Alotaibi, Shujaat Hussain, Aziz Alotaibi, Vladimir Jotsov
2022 Applied Sciences  
The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm.  ...  Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN).  ...  In addition, the authors would like to thank the deanship of scientific research at Shaqra University for supporting this work. Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/app12010504 fatcat:lz3s6gn7nfexhknznownowa3ay

SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection [article]

Vaibhav Kumar, Mrinal Dhar, Dhruv Khattar, Yash Kumar Lal, Abhimanshu Mishra, Manish Shrivastava, Vasudeva Varma
2018 arXiv   pre-print
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.  ...  We test our model over 2538 posts (having trained it on 17000 records) and achieve an accuracy of 83.49% outscoring previous state-of-the-art approaches.  ...  We used a batch size of 256 and adadelta [21] as a gradient based optimizer for learning the model parameters.  ... 
arXiv:1808.00957v1 fatcat:2zz3dmrvtbdbbdo3jzfftpfhd4

Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter [article]

Abhijnan Chakraborty, Rajdeep Sarkar, Ayushi Mrigen, Niloy Ganguly
2017 arXiv   pre-print
The competition for user attention in such mediums has led many media houses to use catchy sensational form of tweets to attract more users - a process known as clickbaiting.  ...  on Twitter.  ...  [3] used deep learning based techniques to detect clickbaits.  ... 
arXiv:1709.02957v1 fatcat:byvjiul55jfv7knvucaec652hu

A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [article]

Chahat Raj, Priyanka Meel
2021 arXiv   pre-print
Fake news detection is one of the most analyzed and prominent areas of research. These detection techniques apply popular machine learning and deep learning algorithms.  ...  Researchers are analyzing online data based on multiple modalities composed of text, image, video, speech, and other contributing factors.  ...  Real-time Detection: With the assistance of deep learning algorithms, real-time detection models can be built to use fact-checked articles on the web for training and generate predictions for unseen data  ... 
arXiv:2112.00803v1 fatcat:twppg5v37bdozcdloaa6zfk7s4

Digital media and misinformation: An outlook on multidisciplinary strategies against manipulation

Danielle Caled, Mário J. Silva
2021 Journal of Computational Social Science  
an inaccurate narrative already assimilated; and (3) an interdisciplinary discussion on different strategies for coping with misinformation.  ...  It includes: (1) a conceptualization of misinformation and related terms, such as rumors and disinformation; (2) an analysis of the cognitive vulnerabilities that hinder the correction of the effects of  ...  The scalability issues occur mainly because a large part of the existing tools depends on human judgment to manually assign labels that are used to train models based on machine learning and deep learning  ... 
doi:10.1007/s42001-021-00118-8 pmid:34075349 pmcid:PMC8156576 fatcat:3uagqf2i2ndpdcxei7wceynxzy

Towards Understanding the Information Ecosystem Through the Lens of Multiple Web Communities [article]

Savvas Zannettou
2019 arXiv   pre-print
Then, we follow a data-driven cross-platform quantitative approach to analyze billions of posts from Twitter, Reddit, 4chan's /pol/, and Gab, to shed light on: 1) how news and memes travel from one Web  ...  community to another and how we can model and quantify the influence between Web communities; 2) characterizing the role of emerging Web communities and services on the Web, by studying Gab and two Web  ...  Similarly, Zannettou et al. [334] use deep learning techniques to detect clickbaits on YouTube.  ... 
arXiv:1911.10517v1 fatcat:piuwv7zv7zghlof5tqhuhnukla

Online social networks security and privacy: comprehensive review and analysis

Ankit Kumar Jain, Somya Ranjan Sahoo, Jyoti Kaubiyal
2021 Complex & Intelligent Systems  
The pivotal reason behind this phenomenon happens to be the ability of OSNs to provide a platform for users to connect with their family, friends, and colleagues.  ...  There are numerous security and privacy issues related to the user's shared information especially when a user uploads personal content such as photos, videos, and audios.  ...  [102] proposed a model that detects bot net using adaptive multilayered-based machine learning approach.  ... 
doi:10.1007/s40747-021-00409-7 fatcat:s4mc4ydaa5hdhpgghwqsjmyruq

State of AI Ethics Report (Volume 6, February 2022) [article]

Abhishek Gupta
2022 arXiv   pre-print
and Imagination for Preventing AI Harms".  ...  It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.  ...  The surging attention to AI Ethics is based on a fundamentally similar realisation.  ... 
arXiv:2202.07435v1 fatcat:wbalu3j3ynelfim7eqvxcpwu3q

Same Water, Difference Dreams: Salient Lessons of the Sino-Japanese War for Future Naval Warfare

Andrew Rhodes
2020 MCU Journal  
The Sino-Japanese War of 1894–95 remains a cautionary tale full of salient lessons for future conflict.  ...  American officers considering the role of the sea Services in a future war must understand the history and organizational culture of the Chinese military and consider how these factors shape the Chinese  ...  The cross-case study analysis will draw on the iron triangle model and numerous other studies that have been based on that theoretical foundation.  ... 
doi:10.21140/mcuj.20201102002 fatcat:i75a5j73pbd4vax543d74t2i6a

Narrative Mechanics: Strategies and Meanings in Games and Real Life [article]

Beat Suter, René Bauer, Mela Kocher, Et Al.
2021 Zenodo  
This book identifies narrative strategies as mechanisms for meaning and manipulation in games and real life.  ...  They occur as texts, recipes, stories, dramas in three acts, movies, videos, tweets, journeys of heroes, but also as rewarding stories in games and as narratives in society – such as a career from rags  ...  a deep-rooted system of signs and symbolism" (ibid 44).  ... 
doi:10.5281/zenodo.5821920 fatcat:t3hd6qe2prc4rfstaepnrwssbi

D1.3 Cyberthreats and countermeasures

Mark Ryan, Kevin Macnish, Tally Hatzakis
2019
This situation parallels to vulnerability disclosure, where researchers often need to make a trade-off between disclosing a vulnerability publicly (opening it up for potential abuse) and not disclosing  ...  The arrival of new technologies can cause changes and create new risks for society (Zwetsloot and Dafoe, 2019) (Shushman et al., 2019), even when they are not deliberately misused.  ...  A similar process is repeated for the "dog" shadow model, and so on.  ... 
doi:10.21253/dmu.7951292.v1 fatcat:w3z55dymsjcwfkhp7opx4pvhui

Computational Methods in the Study of Individuals' Attention Online

Nir Grinberg
2017
As consumption of information shifts to digital means, systems are playing a increasing role in shaping both the information we pay attention to and the practices for paying attention.  ...  Overall, this dissertation lays the foundation for assessing the impact information systems have on human attention, and provides guidelines for the design of better information systems in the future.  ...  Clickbaits lure individuals to visit the story page more often than other stories, produce higher click-through rates, and lead recommendation systems to further increase exposure of Clickbaits to other  ... 
doi:10.7298/x41r6nnt fatcat:wu55dtc6lrg65lafvoa76gyosq
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