RUN-AS: a novel approach to annotate news reliability for disinformation detection.
The development of the internet and digital technologies has inadvertently facilitated the huge disinformation problem that faces society nowadays. This phenomenon impacts ideologies, politics and public health. The 2016 US presidential elections, the Brexit referendum, the COVID-19 pandemic and the...
| Published in: | Language Resources & Evaluation Vol. 58; no. 2; pp. 609 - 640 |
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| Main Authors: | , , , , |
| Format: | Article |
| Published: |
Springer Nature
Jun2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178064681&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178064681 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2024 vid: 58 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 178064681 10.1007/s10579-023-09678-9 ppf: 609 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: RUN-AS: a novel approach to annotate news reliability for disinformation detection. aug: au: Bonet-Jover, Alba Sepúlveda-Torres, Robiert Saquete, Estela Martínez-Barco, Patricio Nieto-Pérez, Mario affil: https://ror.org/05t8bcz72 Department of Software and Computing Systems, University of Alicante, carretera San Vicente s/n, 03690, San Vicente del Raspeig, Alicante, Spain su: Brexit Referendum, 2016 United States presidential election, 2016 Public health & politics Disinformation Russian invasion of Ukraine, 2022- Ideology Deep learning Ontology Ukraine Russia sug: subj: Ukraine Russia Brexit Referendum, 2016 United States presidential election, 2016 Public health & politics Disinformation Russian invasion of Ukraine, 2022- Ideology Deep learning Ontology keyword: Annotation guideline Dataset annotation Disinformation detection Natural language processing Reliability detection ab: The development of the internet and digital technologies has inadvertently facilitated the huge disinformation problem that faces society nowadays. This phenomenon impacts ideologies, politics and public health. The 2016 US presidential elections, the Brexit referendum, the COVID-19 pandemic and the Russia-Ukraine war have been ideal scenarios for the spreading of fake news and hoaxes, due to the massive dissemination of information. Assuming that fake news mixes reliable and unreliable information, we propose RUN-AS (Reliable and Unreliable Annotation Scheme), a fine-grained annotation scheme that enables the labelling of the structural parts and essential content elements of a news item and their classification into Reliable and Unreliable. This annotation proposal aims to detect disinformation patterns in text and to classify the global reliability of news. To this end, a dataset in Spanish was built and manually annotated with RUN-AS and several experiments using this dataset were conducted to validate the annotation scheme by using Machine Learning (ML) and Deep Learning (DL) algorithms. The experiments evidence the validity of the annotation scheme proposed, obtaining the best F 1 m , 0.948, with the Decision Tree algorithm. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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