  An increasing number of people now rely on online platforms to meet their
health information needs. Thus identifying inconsistent or conflicting textual
health information has become a safety-critical task. Health advice data poses
a unique challenge where information that is accurate in the context of one
diagnosis can be conflicting in the context of another. For example, people
suffering from diabetes and hypertension often receive conflicting health
advice on diet. This motivates the need for technologies which can provide
contextualized, user-specific health advice. A crucial step towards
contextualized advice is the ability to compare health advice statements and
detect if and how they are conflicting. This is the task of health conflict
detection (HCD). Given two pieces of health advice, the goal of HCD is to
detect and categorize the type of conflict. It is a challenging task, as (i)
automatically identifying and categorizing conflicts requires a deeper
understanding of the semantics of the text, and (ii) the amount of available
data is quite limited.
  In this study, we are the first to explore HCD in the context of pre-trained
language models. We find that DeBERTa-v3 performs best with a mean F1 score of
0.68 across all experiments. We additionally investigate the challenges posed
by different conflict types and how synthetic data improves a model's
understanding of conflict-specific semantics. Finally, we highlight the
difficulty in collecting real health conflicts and propose a human-in-the-loop
synthetic data augmentation approach to expand existing HCD datasets. Our HCD
training dataset is over 2x bigger than the existing HCD dataset and is made
publicly available on Github.
