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.

Upadhyay, R., Pasi, G., Viviani, M. (2021). Health Misinformation Detection in Web Content: A Structural-, Content-based, and Context-aware Approach based on Web2Vec. In GoodIT '21: Proceedings of the Conference on Information Technology for Social Good (pp.19-24). Association for Computing Machinery, Inc [10.1145/3462203.3475898].

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

Upadhyay, Rishabh;Pasi, Gabriella;Viviani, Marco
2021

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.
slide + paper
Credibility; Deep Learning; Health Misinformation; Machine Learning; Social Web;
English
1st Conference on Information Technology for Social Good, GoodIT 2021 - 9 September 2021 through 11 September 2021
2021
GoodIT '21: Proceedings of the Conference on Information Technology for Social Good
978-145038478-0
2021
19
24
none
Upadhyay, R., Pasi, G., Viviani, M. (2021). Health Misinformation Detection in Web Content: A Structural-, Content-based, and Context-aware Approach based on Web2Vec. In GoodIT '21: Proceedings of the Conference on Information Technology for Social Good (pp.19-24). Association for Computing Machinery, Inc [10.1145/3462203.3475898].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/326678
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