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Vec4Cred: a model for health misinformation detection in web pages.
Upadhyay, Rishabh; Pasi, Gabriella; Viviani, Marco.
  • Upadhyay R; Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Edificio U14 - ABACUS, Viale Sarca, 336, Milan, 20126 Italy.
  • Pasi G; Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Edificio U14 - ABACUS, Viale Sarca, 336, Milan, 20126 Italy.
  • Viviani M; Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Edificio U14 - ABACUS, Viale Sarca, 336, Milan, 20126 Italy.
Multimed Tools Appl ; : 1-20, 2022 Jul 28.
Article in English | MEDLINE | ID: covidwho-2236501
ABSTRACT
Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Multimed Tools Appl Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Multimed Tools Appl Year: 2022 Document Type: Article