  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.
