# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 10 Discussion and Future Work  
Fact-checking, especially in the rapidly evolving domain of medical misinformation, involves a delicate balance between speed, accuracy, and scalability. Manual fact-checking benefits from human expertise, the understanding of details, and higher accuracy, especially for complex or nuanced claims. However, it is time-consuming, expensive, and prone to bias, as human fact-checkers may unintentionally or intentionally introduce personal biases that affect the outcomes [13; 15]. In contrast, automated fact-checking offers speed, scalability, and consistency, as it can quickly process large volumes of data and maintain a uniform approach in evaluating facts, free from human biases. Once developed, automated systems require fewer resources compared to the large teams needed for manual fact-checking. However, they often struggle with understanding nuanced contexts, sarcasm, or cultural references, and can produce false positives or negatives, especially when the algorithm is poorly trained or when claims require expert analysis. Moreover, different automated fact-checking methods come with their own strengths and limitations. Content-based methods analyze textual or visual elements to detect misinformation, offering independence from external metadata but struggling with subtle contextual manipulation and requiring extensive labeled data. Propagation-based methods leverage the spread patterns of information, making them effective against coordinated disinformation campaigns, yet they rely on large-scale social network data. Source-based methods assess the credibility of information sources, providing an efficient way to filter unreliable content but facing challenges when dealing with new or not widely recognized sources. In recent years, research advances in addressing misinformation, and specifically health misinformation, were expedited due to the COVID-19 pandemic. However, there are still several open issues and major challenges in the domain of medical misinformation, including the following areas [18]: (a) the identification of dominant health misinformation trends and their prevalence across different social platforms, (b) the mechanisms that allow the spread of health misinformation, (c) their impact on the development and reproduction of unhealthy and dangerous behaviors, and finally, (d) the development of strategies for fighting and reducing the negative impact of misinformation without reducing the inherent communicative potential to propagate health information. Addressing the various open problems in these areas requires the collaboration of _cross-discipline_ researchers, policymakers, and technology experts. One challenge specific to the medical domain is the _inherent uncertainty_ of medical scientific knowledge and the fact that scientific knowledge _evolves over time_[238]. Medical facts are usually accompanied by probabilities and risk factors that may differ based on population demographics. Furthermore, medical knowledge evolves over time as new discoveries are made. This uncertainty poses additional challenges in communicating medical information to the public and provides a fertile ground for misinformation to grow. Uncertainty and temporality should be accounted for when designing approaches for fact-checking and misinformation detection or mitigation. Another challenge is the fact that the approaches used for fake news fabrication and propagation in social media and networks are constantly improving. Aiming at accurately imitating the characteristics and features of valid news as closely as possible, the deployed strategies are becoming more complex and refined. The recent advancements in large language models (LLMs), which can be trained to follow the style and form of valid news will only complicate the problem [239]. Consequently, the assumption that fake news has distinct characteristics that separate it from valid news weakens, as producers and promoters of fake news deploy more advanced techniques.