Health Misinformation Detection in Web Content via Web2Vec: A Structural-, Content-based, and Context-aware Approach based on Web2Vec
<|reference_start|>Health Misinformation Detection in Web Content via Web2Vec: A Structural-, Content-based, and Context-aware Approach based on Web2Vec: 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.<|reference_end|>
@article{upadhyay2024health,
    title={Health Misinformation Detection in Web Content via Web2Vec: A
  Structural-, Content-based, and Context-aware Approach based on Web2Vec},
    author={Rishabh Upadhyay, Gabriella Pasi, and Marco Viviani},
    journal={arXiv preprint arXiv:2407.07914},
    year={2024},
    doi={10.1145/3462203.3475898},
    archivePrefix={arXiv},
    eprint={2407.07914},
    primaryClass={cs.IR}
}