Department of Informatics, Systems, and Communication (DISCo), University of Milano-Bicocca, Edificio U14-ABACUS, Viale Sarca, 336, 20126 Milan, Italy.
Int J Environ Res Public Health. 2022 Feb 15;19(4):2173. doi: 10.3390/ijerph19042173.
The increasing availability of online content these days raises several questions about effective access to information. In particular, the possibility for almost everyone to generate content with no traditional intermediary, if on the one hand led to a process of "information democratization", on the other hand, has negatively affected the genuineness of the information disseminated. This issue is particularly relevant when accessing health information, which impacts both the individual and societal level. Often, laypersons do not have sufficient health literacy when faced with the decision to rely or not rely on this information, and expert users cannot cope with such a large amount of content. For these reasons, there is a need to develop automated solutions that can assist both experts and non-experts in discerning between genuine and non-genuine health information. To make a contribution in this area, in this paper we proceed to the study and analysis of distinct groups of features and machine learning techniques that can be effective to assess misinformation in online health-related content, whether in the form of Web pages or social media content. To this aim, and for evaluation purposes, we consider several publicly available datasets that have only recently been generated for the assessment of health misinformation under different perspectives.
如今，越来越多的在线内容引发了人们对于有效获取信息的几个问题。特别是，几乎任何人都可以在没有传统中介的情况下生成内容，如果一方面导致了“信息民主化”的过程，另一方面又对传播的信息的真实性产生了负面影响。当涉及到健康信息时，这个问题尤其重要，因为它会影响到个人和社会层面。通常情况下，非专业人士在决定是否依赖这些信息时，健康素养可能不足，而专家用户也无法应对如此大量的内容。出于这些原因，需要开发自动化解决方案，以便专家和非专业人士能够区分真实和非真实的健康信息。为了在这一领域做出贡献，在本文中，我们对可以有效评估在线健康相关内容（无论是网页形式还是社交媒体内容）中的错误信息的不同特征组和机器学习技术进行了研究和分析。为此，我们考虑了几个最近才为从不同角度评估健康错误信息而生成的公开可用数据集。
