Finding common features in multilingual fake news: a quantitative clustering approach.
Since the Internet is a breeding ground for unconfirmed fake news, its automatic detection and clustering studies have become crucial. Most current studies focus on English texts, and the common features of multilingual fake news are not sufficiently studied. Therefore, this article uses English, Ru...
| Publicado en: | Digital Scholarship in the Humanities Vol. 39; no. 2; pp. 790 - 805 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Jun2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=177947261&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 177947261 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2024 vid: 39 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 177947261 10.1093/llc/fqae016 ppf: 790 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.9MB tig: atl: Finding common features in multilingual fake news: a quantitative clustering approach. aug: au: Yuan, Wei Liu, Haitao affil: School of International Relations, National University of Defense Technology , Nanjing, 210039, China Institute of Quantitative Linguistics, Beijing Language and Culture University , Beijing, 100083, China Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies , Guangzhou, 510420, China Department of Linguistics, Zhejiang University , Hangzhou, 310058, China su: Fake news Principal components analysis K-means clustering Hierarchical clustering (Cluster analysis) Self-expression Fuzzy clustering technique sug: subj: Fake news Principal components analysis K-means clustering Hierarchical clustering (Cluster analysis) Self-expression Fuzzy clustering technique keyword: Chinese English fake news quantitative analysis Russian ab: Since the Internet is a breeding ground for unconfirmed fake news, its automatic detection and clustering studies have become crucial. Most current studies focus on English texts, and the common features of multilingual fake news are not sufficiently studied. Therefore, this article uses English, Russian, and Chinese as examples and focuses on identifying the common quantitative features of fake news in different languages at the word, sentence, readability, and sentiment levels. These features are then utilized in principal component analysis, K-means clustering, hierarchical clustering, and two-step clustering experiments, which achieved satisfactory results. The common features we proposed play a greater role in achieving automatic cross-lingual clustering than the features proposed in previous studies. Simultaneously, we discovered a trend toward linguistic simplification and economy in fake news. Furthermore, fake news is easier to understand and uses negative emotional expressions in ways that real news does not. Our research provides new reference features for fake news detection tasks and facilitates research into their linguistic characteristics. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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