# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 2 Overview
Another major issue regarding medical information, and scientific information in general, is the fact that citizens often have a limited understanding of basic scientific facts and more broadly of the scientific process [23]. Moreover, personal beliefs are often inconsistent with the best available science due to inaccurate perceptions, lack of scientific consensus, or adoption of conspiracy theories. Fake news propagation in social media is also amplified by the fact that it is systemic according to [24]. Fake news is designed in such a way so as to pass itself as news to the relevant targeted audience and in fact mislead it by exploiting the systemic features inherent in the channels of social media. Such features include various cognitive biases and heuristics that lead to increasing the spread of fake news propagation. Various pilot studies have been conducted regarding the typology of health misinformation in social media. The authors of [25] examined health-related misinformative posts from various social network platforms written in Polish during the period 2012-2017. In the initial screening, satire, parody, and propaganda were not detected, probably because they mainly apply to political news. However, 40% of the most frequently shared links contained text classified as fake news. The most fallacious content concerned vaccines while content about cardiovascular diseases was, in general, well sourced and informative. Another study of the sources and types of misinformation about COVID-19 highlighted the prevalence of fake news in social media [26]. In this study, the identified common types of misinformation including false claims, conspiracy theories, and pseudoscientific health therapies, regarding the diagnosis, treatment, prevention, origin, and spread of the virus. Finally, in a high-level overview survey on English social media [18], the authors used the PubMed search engine to explore the prevalence of health misinformation and identify the medical topics that are more susceptible to fake information. They reported high misinformation content in the smoke, drug, and vaccine categories; moderate misinformation about diets and noncommunicable diseases and pandemics; and low misinformation in medical treatments and surgical treatments, since in this case most information is coming from official accounts. In the following sections, we focus on fact-checking, fake news detection, and mitigation in social media platforms with emphasis on the medical domain. _Fact-checking_ is the process of verifying the accuracy and truthfulness of information that is presented as news or as factual. Fact-checking involves researching the claims being made, looking for credible sources of information, and comparing the information to other sources to ensure that it is accurate and truthful. We categorize fact-checking approaches into manual and automatic approaches and present a thorough analysis of the related methods. Fact-checking is important because it helps to prevent the spread of misinformation and it allows people to make informed decisions based on accurate information. We also discuss the available fact-checking tools and services. In terms of fake news detection, we provide a systematic categorization and thorough description and analysis of fake news detection approaches. We categorize fake news detection approaches into those using (a) the content of the news item, (b) information about its propagation in the social platform, and (c) features of the source; that is, of the publisher or the social media users involved in the propagation. For each approach, we describe in depth the used methods, including the commonly used features in the case of traditional machine learning (ML) approaches and the involved pipelines in the case of deep learning (DL) approaches.