Results 31 to 40 of about 387 (173)
Context-Aware Deep Markov Random Fields for Fake News Detection
IEEE Access, 2021 Fake news is a serious problem, which has received considerable attention from both industry and academic communities. Over the past years, many fake news detection approaches have been introduced, and most of the existing methods rely on either news ...
Tien Huu Do +4 more
doaj +1 more source
Complex Network and Source Inspired COVID-19 Fake News Classification on Twitter
IEEE Access, 2021 In COVID-19 related infodemic, social media becomes a medium for wrongdoers to spread rumors, fake news, hoaxes, conspiracies, astroturf memes, clickbait, satire, smear campaigns, and other forms of deception.
Khubaib Ahmed Qureshi +3 more
doaj +1 more source
From Attachments to SEO: Click Here to Learn More about Clickbait PDFs! [PDF]
, 2023 Clickbait PDFs are PDF documents that do not embed malware but trick victims into visiting malicious web pages leading to attacks like password theft or drive-by download.
Graziano, Mariano +5 more
core +4 more sources
Heuristic Feature Selection for Clickbait Detection
CoRR, 2018 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 (no clickbait) to 1 (strong clickbait).
Matti Wiegmann +4 more
openaire +2 more sources
Clickbait detection using word embeddings
CoRR, 2017 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 thumbnail pictures to attract click-throughs and to encourage forwarding of the material over online social networks.
Vijayasaradhi Indurthi, Subba Reddy Oota
openaire +2 more sources
GR-405 Boosting Clickbait Detection through Semantic Insights and Attention-Driven Neural Network [PDF]
, 2023 The digital age has witnessed an explosion of online content, making it increasingly challenging for users to differentiate between reliable information and clickbait, which is often misleading or sensationalized.
Meesala, Lokesh
core
The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube [PDF]
2018 IEEE Security and Privacy Workshops (SPW), 2018 The use of deceptive techniques in user-generated video portals is ubiquitous. Unscrupulous uploaders deliberately mislabel video descriptors aiming at increasing their views and subsequently their ad revenue. This problem, usually referred to as "clickbait," may severely undermine user experience.
Savvas Zannettou +3 more
openaire +2 more sources
Webis Clickbait Corpus 2016 (Webis-Clickbait-16)
, 2016 <p>The Webis Clickbait Corpus 2016 (Webis-Clickbait-16) comprises 2992 Twitter tweets sampled from top 20 news publishers as per retweets in 2014.
Stein, Benno (5169011) +7 more
core +1 more source
Hybridizing metric learning and case-based reasoning for adaptable clickbait detection. [PDF]
, 2018 [EN]The term clickbait is usually used to name web contents which are specifically designed to maximize advertisement monetization, often at the expense of quality and exactitude.
Revuelta Herrero, Jorge +3 more
core +1 more source
Federated Hierarchical Hybrid Networks for Clickbait Detection
CoRR, 2019 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 more and more attention, researchers have started to explore machine learning techniques to automatically detect clickbaits.
Feng Liao +3 more
openaire +2 more sources