# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 1 Introduction
There was a strong effort from the scientific community to construct and make publicly available datasets with valid scholarly information relevant to the pandemic and coronaviruses in general. A well-known dataset is the COVID-19 Open Research Dataset (CORD-19) [9] and the Covidex search engine [10] built on top of it. Infodemic risk indices, such as those developed by the COVID-19 Infodemic Observatory ([https://covid19 obs.fbk.eu/](https://covid19 obs.fbk.eu/), accessed on 10 December 2024), offer a quantitative framework for assessing the prevalence and severity of misinformation. These indices aid in identifying high-risk regions, topics, and patterns, enabling targeted interventions to mitigate the spread of misleading information. Most fact-checking organizations had to grow and dedicate more of their resources to COVID-19 misinformation [11]. As a result, several fact-checking services were developed, aiming at assessing the validity of claims and the credibility of sources related to the pandemic. However, it has become increasingly challenging and expensive to identify fake news and claims through manual inspection, due to the speed at which information (both valid and non-valid) spreads in the media, and especially on social media. In a WHO survey on COVID-19 fact-checkers (WHO cooperates with a global network of 200 fact-checking organizations [https://covid19 misinfo.org/fact-checking/covid-19-fact-checkers/](https://covid19 misinfo.org/fact-checking/covid-19-fact-checkers/), accessed on 10 December 2024), most fact-checking organizations reported that their leading challenge was keeping up with rapidly changing science and fact-checking health misinformation [12]. The inability of manual approaches to deal with the scale of the problem highlights the importance of automatic, data-driven approaches that can complement or supplant manual efforts. The research efforts of the information technology scientific community for addressing misinformation focus on a variety of issues, such as automating claim detection and validation, detecting misinformation using the content and the propagation patterns, identifying users (malicious or not) that instigate or facilitate misinformation, as well as mitigating misinformation. Most of the approaches develop and apply state-of-the-art data science and machine learning techniques, trained on large amounts of data. The research on misinformation is extremely broad, touching several scientific fields. Previous technical surveys on fact-checking and fake news provide a general overview of the current landscape but do not target the medical domain [13; 14; 15]. The few surveys that explicitly focus on misinformation in the medical domain [16; 17; 18; 19] provide a high-level approach that is relevant to a broad spectrum of researchers, stakeholders, and decision makers. This survey complements the previous works by focusing on the medical domain and exploring how the different fact-checking and fake news detection techniques have been adapted to this domain from a computer engineering perspective. To facilitate future research on the area, it thoroughly describes publicly available domain-specific datasets, paying special attention to the COVID-19 case. We followed an exploratory (snowball) methodology for collecting papers on fact-checking and fake news detection on medical issues. The papers were gathered through Google Scholar by submitting appropriate queries such as _\"medical fake news detection\"_, _\"health misinformation detection on social media\"_, _\"detection of COVID-19 vaccine misinformation\"_, _\"medical fact check\"_, etc. We also used authoritative general surveys on fake news and fact-checking [13; 14; 15] as a starting point for exploring the literature and identifying medical-related approaches. Finally, we explored the citations of the most cited papers in order to cover the most recent advancements in the area. In total, this survey references 190 scientific papers and describes 24 publicly available datasets and 11 fact-checking tools.