research-article

Health Misinformation Detection in Web Content: A Structural-, Content-based, and Context-aware Approach based on Web2Vec

Authors:

Rishabh Upadhyay,

Gabriella Pasi,

Marco VivianiAuthors Info & Claims

GoodIT '21: Proceedings of the Conference on Information Technology for Social Good

Pages 19 - 24

https://doi.org/10.1145/3462203.3475898

Published: 09 September 2021 Publication History

Abstract

In recent years, we have witnessed the proliferation of large amounts of online content generated directly by users with virtually no form of external control, leading to the possible spread of misinformation. The search for effective solutions to this problem is still ongoing, and covers different areas of application, from opinion spam to fake news detection. A more recently investigated scenario, despite the serious risks that incurring disinformation could entail, is that of the online dissemination of health information.

Early approaches in this area focused primarily on user-based studies applied to Web page content. More recently, automated approaches have been developed for both Web pages and social media content, particularly with the advent of the COVID-19 pandemic. These approaches are primarily based on handcrafted features extracted from online content in association with Machine Learning. In this scenario, we focus on Web page content, where there is still room for research to study structural-, content- and context-based features to assess the credibility of Web pages.

Therefore, this work aims to study the effectiveness of such features in association with a deep learning model, starting from an embedded representation of Web pages that has been recently proposed in the context of phishing Web page detection, i.e., Web2Vec.

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Cited By

Zhang BNaderi NMishra RTeodoro D(2024)Online Health Search Via Multidimensional Information Quality Assessment Based on Deep Language Models: Algorithm Development and ValidationJMIR AI10.2196/426303(e42630)Online publication date: 2-May-2024
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Zhang YZong RShang LYue ZZeng HLiu YWang D(2024)Tripartite Intelligence: Synergizing Deep Neural Network, Large Language Model, and Human Intelligence for Public Health Misinformation Detection (Archival Full Paper)Proceedings of the ACM Collective Intelligence Conference10.1145/3643562.3672613(63-75)Online publication date: 27-Jun-2024
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cover image ACM Conferences

GoodIT '21: Proceedings of the Conference on Information Technology for Social Good

September 2021

345 pages

ISBN:9781450384780

DOI:10.1145/3462203

Copyright © 2021 ACM.

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Published: 09 September 2021

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GoodIT '21: Conference on Information Technology for Social Good

September 9 - 11, 2021

Roma, Italy

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Cited By

Zhang BNaderi NMishra RTeodoro D(2024)Online Health Search Via Multidimensional Information Quality Assessment Based on Deep Language Models: Algorithm Development and ValidationJMIR AI10.2196/426303(e42630)Online publication date: 2-May-2024
https://doi.org/10.2196/42630
Zhang YZong RShang LYue ZZeng HLiu YWang D(2024)Tripartite Intelligence: Synergizing Deep Neural Network, Large Language Model, and Human Intelligence for Public Health Misinformation Detection (Archival Full Paper)Proceedings of the ACM Collective Intelligence Conference10.1145/3643562.3672613(63-75)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3643562.3672613
Tang ZGoh DLee CYang Y(2024)Understanding strategies employed by seniors in identifying deepfakesAslib Journal of Information Management10.1108/AJIM-03-2024-0255Online publication date: 21-Nov-2024
https://doi.org/10.1108/AJIM-03-2024-0255
Baqraf YKeikhosrokiani P(2024)Health information quality assessment using artificial intelligence: Quality dimensions from healthcare professionals’ perspectiveAdvances in Intelligent Computing Techniques and Applications10.1007/978-3-031-59711-4_1(1-14)Online publication date: 30-Jun-2024
https://doi.org/10.1007/978-3-031-59711-4_1
Baqraf YKeikhosrokiani PAl-Rawashdeh M(2023)Evaluating online health information quality using machine learning and deep learning: A systematic literature reviewDIGITAL HEALTH10.1177/205520762312122969Online publication date: 20-Nov-2023
https://doi.org/10.1177/20552076231212296
Petrocchi MViviani M(2023)Report on the 2nd Workshop on Reducing Online Misinformation through Credible Information Retrieval (ROMCIR 2022) at ECIR 2022ACM SIGIR Forum10.1145/3582524.358253656:1(1-9)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3582524.3582536
Baqraf YKeikhosrokiani P(2023)The Prediction of Health Information Quality Perception Using Machine Learning and Deep Learning Techniques2023 11th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT58202.2023.10262623(104-109)Online publication date: 23-Aug-2023
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Schlicht IFernandez EChulvi BRosso P(2023)Automatic detection of health misinformation: a systematic reviewJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04619-415:3(2009-2021)Online publication date: 27-May-2023
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Di Sotto SViviani M(2022)Health Misinformation Detection in the Social Web: An Overview and a Data Science ApproachInternational Journal of Environmental Research and Public Health10.3390/ijerph1904217319:4(2173)Online publication date: 15-Feb-2022
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Dixon EAnderson JBlackwelder DL. Radnofsky MLazar A(2022)Barriers to Online Dementia Information and MitigationProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517554(1-14)Online publication date: 29-Apr-2022
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