Towards reliable automated fact-checking in Public Health

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