# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 10 Discussion and Future Work
There are similar challenges for propagation-based methods where bots can be adapted to imitate the structural, temporal, and propagation patterns of valid news propagation networks. However, faithfully reproducing human behavior is more complex, so these features can still preserve some of their discriminatory power. An important future research direction for addressing this challenge is to incorporate available authoritative ground truth knowledge and apply symbolic logic in fake news detection. Specifically for the medical domain, hybrid approaches are needed that combine the constantly evolving machine learning approaches for detection, with the slower-evolving but high-quality and validated information stored in domain-specific knowledge bases. Such information usually originates from authoritative academic research and can be exploited by logic rules and reasoning. The fusion of the statistical learning capabilities of the deep neural networks with strict logic reasoning approaches and rules (e.g., [240]) will lead to a representation learning system with a joint learning mechanism that will achieve joint inference and will enforce complicated correlations and constraints in the output space. Regarding the focus of research on medical fake news, most work targets fake news detection. However, detection needs to be combined with mitigation approaches, which act as gatekeepers of misinformation propagation. Greater research emphasis is needed on the development, refinement, and real-world evaluation of such mitigation approaches. In the same direction, the research community should work toward preemptive approaches that provide valid and fact-checked information, personalized to the current interests of users. The goal is to proactively inform a user about the facts of a misinformation-infested topic, which could potentially attract the attention of the user in the near future. This way, the user will have a critical and well-informed view if and when exposed to this misinformation. Furthermore, inoculated users will act as deterrents to the propagation and spread of misinformation in the social network. The deployment of automated approaches, particularly in the sensitive domain of healthcare, raises significant ethical considerations. Since these tools rely on vast amounts of user data to detect and mitigate misinformation, they can infringe on individual privacy if not handled transparently and responsibly. Additionally, the risk of legitimate content being mistakenly flagged or suppressed poses a threat to freedom of speech and the potential for censorship. To address these challenges, using anonymized data when possible along with eXplainable AI (XAI) [241] plays a crucial role in increasing public trust. By making the algorithms transparent and interpretable, XAI can help users understand why certain content is flagged, fostering accountability and reducing skepticism. However, balancing misinformation mitigation with freedom of speech remains a delicate task, and algorithms must be designed to prioritize fairness and transparency, ensuring that they do not disproportionately target specific viewpoints or communities. As a result, we advocate that there is a pressing need for approaches that provide transparency and integrate explainability and fairness guarantees to build a relationship of trust between the producers, the consumers, and the gatekeepers of the information flow (i.e., the social media platforms). The goal should be the creation of a healthy ecosystem around grounded information that does not hinder freedom of speech, democratic values, diversity, and exploration. Finally, an important finding of this work is that there is not a single specific dataset in the domain of medical fake news that can be used as a benchmark. Most of the described approaches introduce their own customized datasets, making the comparison of the proposed methods difficult. Moreover, the quality of the gathered data and their annotation varies and is sometimes questionable.