Towards reliable automated fact-checking in Public Health

Interest in fact-checking has grown over the past few years with the proliferation of fake news in the
media ecosystem. Indeed, researchers are exploring how fact-checking can be automated using the
latest advances in Machine Learning and Natural Language Processing to combat misinformation
spread. So far, most of the work in this field has focused on verifying claims in politics with little
interest in public health despite the harm that could result from believing incorrect information in this
domain. The purpose of this thesis is to develop the research in automated fact-checking for public
health claims. We would start by creating the first dataset for fact-checking against a large knowledge
source for such claims. This would facilitate the research in the field by providing the community
with the required data to explore the topic. From this dataset, there are many exciting avenues for
research that we wish to explore. We propose multiple modeling ideas that culminate in building
an end-to-end fact-checking system for public health claims. We hope that this work will mark a
significant development in an exciting effort to combat misinformation in public health.