Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations

TL;DR

This paper presents a question-answering system designed to address COVID-19 related inquiries by leveraging NLP technologies, aiming to combat misinformation and information overload during the pandemic.

Contribution

It introduces a novel NLP-based question-answering system for pandemic situations, grounded in design science and socio-technical principles, with a prototype built on the CORD-19 dataset.

Findings

The system effectively answers COVID-19 questions with expert-validated accuracy.

Design principles improve answer relevance and credibility.

Prototype demonstrates practical utility in pandemic information retrieval.

Abstract

The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen's cognitive model of information retrieval interaction to inform our design process from a socio-technical lens. On this basis, we derived prescriptive design knowledge in terms of design requirements and design principles, which we translated into the construction of a prototypical instantiation. Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts.