Spreading of health misinformation is a particular subtype of a broader phenomenon called antisocial behavior, which presents one of the most current and serious problems, which significantly threatens not only the principles on which the web was built, but also has a critical overreach to society. Specifically in the healthcare domain, misinformation cases (such as a wide-spread, but unsupported belief that vaccination causes autism) are very common since they emerge as the results of incorrect interpretation of various medical studies. However, despite the recent research progress in this domain, detection and debunking of misinformation in general provide a number of open problems that need to be addressed by scientists.
It is especially true for the healthcare domain, since most of the existing research focus on political fake news or fake reviews, while the research on healthcare false information is only at the beginning. At the same time, the existing methods aimed at political fake news are not directly applicable in the health domain because of several significant differences which provide also new opportunities. In the MISDEED project, we focus on researching models and methods for automatic detection of medical misinformation. We aim to model misinformation dynamics, research means of involving human experts (medical doctors) in the process of learning of data-driven methods and provide effective ways of presenting fact-checking arguments as a part of misinformation mitigation efforts.
The main goal of the project is to research new knowledge in informatics and information technologies, especially: