From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles
We present a system for the detection of the stance of headlines with regard to their corresponding article bodies. The approach can be applied in fake news, especially clickbait detection scenarios. The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information. We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines. On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 89.59.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesClickbait DetectionFake News DetectionRumour DetectionSimilar Papers 제목 키워드 기반
Automatic Detection of Hungarian Clickbait and Entertaining Fake News
Online news do not always come from reliable sources and they are not always even realistic. The constantly growing number of online textual data has raised the need for detecting deception and bias in texts from differe…
A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
The emergence of social media as news sources has led to the rise of clickbait posts attempting to attract users to click on article links without informing them on the actual article content. This paper presents our eff…
Clickbait DetectionFake News Detectionfeature selectionGeneral ClassificationPrompt-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 DetectionOn Unifying Misinformation Detection
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news…
Few-Shot LearningMisinformation