Results 61 to 70 of about 283 (152)
KazFakeCorpus: A Bilingual Corpus with Multi-Level Semantic Annotation for Fake News Detection
Big Data and Cognitive ComputingThis paper addresses the lack of bilingual annotated resources for automatic fake news detection in the Kazakh–Russian media space, as well as the limitations of binary annotation, which does not always allow disinformation to be represented as a complex
Zhanar Lamasheva +5 more
doaj +1 more source
Clickbait 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, Large Language Models (LLMs) have emerged as a powerful instrument and achieved tremendous success in a series of NLP downstream ...
Yi Zhu 0006 +5 more
openaire +3 more sources
INTRODUCTION. LETTERS IN THE WEB OF LIFE: TOWARDS AN ECOLOGICAL PHILOLOGY
German Life and Letters, Volume 78, Issue 3, Page 277-288, July 2025.
Conor Brennan, Caitríona Ní Dhúill
wiley +1 more source
The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis. [PDF]
J Med Internet Res, 2023 Maleki N, Padmanabhan B, Dutta K.
europepmc +1 more source
Clickbait Detection in Tweets Using Self-attentive Network
CoRR, 2017 Clickbait detection in tweets remains an elusive challenge. In this paper, we describe the solution for the Zingel Clickbait Detector at the Clickbait Challenge 2017, which is capable of evaluating each tweet's level of click baiting. We first reformat the regression problem as a multi-classification problem, based on the annotation scheme.
openaire +2 more sources
A review on fake news detection 3T's: typology, time of detection, taxonomies. [PDF]
Int J Inf Secur, 2023 Rastogi S, Bansal D.
europepmc +1 more source
Prompt-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 significant negative impacts on the users, which brings users misleading or even click-jacking attacks.
Haoxiang Deng +6 more
openaire +3 more sources
Clickbait Spoiler Detection and Generation
Clickbait content typically uses sensationalized language to entice curiosity and stimulate user interaction, often at the expense of user satisfaction. In this paper, we introduce a spoiler generation system designed to neutralize clickbait by disclosing important information in a clear, informative format. Our system combines three primary components:
openaire +2 more sources
ClickGuard: A Trustworthy Adaptive Fusion Framework for Clickbait Detection
CoRRThe widespread use of clickbait headlines, crafted to mislead and maximize engagement, poses a significant challenge to online credibility. These headlines employ sensationalism, misleading claims, and vague language, underscoring the need for effective detection to ensure trustworthy digital content.
Chhavi Dhiman +4 more
openaire +2 more sources