Explainable online health information truthfulness in Consumer Health Search - Upadhyay, Rishabh; Knoth, Petr; Pasi, Gabriella; Viviani, Marco (Frontiers in Artificial Intelligence, 2023)
People are today increasingly relying on health information they find online to make decisions that may impact both their physical and mental well-being. Therefore, there is a growing need for systems that can assess the truthfulness of such health information. Most of the current literature solutions use machine learning or knowledge-based approaches treating the problem as a binary classification task, discriminating between correct information and misinformation. Such solutions present several problems with regard to user decision-making, among which: (i) the binary classification task provides users with just two predetermined possibilities with respect to the truthfulness of the information, which users should take for granted; indeed, (ii) the processes by which the results were obtained are often opaque and the results themselves have little or no interpretation. To address these issues, we approach the problem as an ad hoc retrieval task rather than a classification task, with reference, in particular, to the Consumer Health Search task. To do this, a previously proposed Information Retrieval model, which considers information truthfulness as a dimension of relevance, is used to obtain a ranked list of both topically-relevant and truthful documents. The novelty of this work concerns the extension of such a model with a solution for the explainability of the results obtained, by relying on a knowledge base consisting of scientific evidence in the form of medical journal articles. We evaluate this approach both quantitatively, as a standard classification task, and qualitatively, by means of a user study over the explained ranked list of search results.