# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 8 Mitigation  
Fake news and misinformation shared on social media can be very effective in shaping the opinions and beliefs of people. Medical fake news, in particular, is a serious danger to public health, as the spread of false information and lack of knowledge of the truth can put patients at risk. The proliferation of medical fake news can cause individuals to adopt dangerous health practices or avoid proven, life-saving medical treatments. As such, it is imperative that the spread of medical fake news on social media is effectively mitigated using robust solutions that can limit its impact on public health. The issue of mitigating fake news has garnered attention from researchers across various fields, including the social sciences and psychology. However, in this survey, we will only describe the technical approaches developed in the field of computer science. One category of mitigation methods revolves around the user perspective, leveraging the diverse roles and positions users can have in the dissemination of fake news. Some users hold significant influence as opinion leaders and have a wide circle of followers while others act as correctors and contribute to fake news posts by attaching links and comments that counter the false information with facts and evidence [14]. Other approaches involve the identification of sources that create and disseminate disinformation, which are afterward prevented from contaminating social media platforms, as described in Section 6. Such approaches are characterized by their proactive nature and can be included in the early fake news detection methods as they aim to identify users who are at risk of engaging with fake news or have previously engaged with it and may do so again in the future. These techniques utilize content-based methods, like the ones described in Section 4, to analyze the language and content patterns of news articles in the user history and their personal information. Through the application of these techniques, it becomes feasible to identify and remove fake users or restrict their movements on social media platforms. An important aspect of these techniques is the identification of influential spreaders. When blocking fake news in a social network, targeting these influential spreaders can lead to a more efficient intervention compared to those with insignificant social influence on others [207]. The bot detection techniques described in Section 6 also belong to this category. These methods focus on identifying and removing fake users that are designed to mimic human behavior and disseminate misinformation by detecting patterns of bot behavior, such as repeated postings of similar content or engagement with other bots. Once detected, these fake users can be removed from social media platforms, limiting their ability to spread false information [171; 186; 192]. Also, the work in [208] presents an innovative approach to effectively mitigate the problem of online misinformation. This study delves into the role of the \"guardian\", online users who actively combat misinformation and fake news during online discussions by providing fact-checking URLs. The authors also introduce a novel fact-checking URL recommendation model, designed to motivate and empower guardians to engage even more actively in fact-checking activities. \begin{table}
\begin{tabular}{l l l l l} \hline
**Work** & **Type** & **URL** & **Annotation** & **Characteristics** \\ \hline \multirow{4}{*}{MM-COVID [206]} & \multirow{4}{*}{-COVID-19-News} & & & A total of 3981 fake and \\  & & & & 7192 trustworthy \\  & & & & \\ \cline{1-1}  & & & & multi-language and \\ \cline{1-1}  & & & & multimodal news, including \\ \cline{1-1}  & & & & temporal information \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._Intervening based on network structure involves stopping the spread of fake news by blocking its propagation paths [207]. This approach relies on analyzing the network structure of how fake news spreads and predicting how it will continue to spread.