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

TL;DR

This paper introduces Web2Vec, a deep learning approach that combines structural, content, and context features to detect health misinformation on web pages, addressing a critical need for credible online health information.

Contribution

It proposes a novel integration of structural, content, and context features with Web2Vec embeddings for improved health misinformation detection on web pages.

Findings

Web2Vec effectively captures web page features for misinformation detection.

The approach outperforms traditional handcrafted feature methods.

Structural, content, and context features enhance detection accuracy.

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.