Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction

TL;DR

This paper introduces CoVaxLies, a new dataset and a graph link prediction approach using knowledge graphs to automatically detect COVID-19 vaccine misinformation on Twitter, outperforming traditional classification methods.

Contribution

It presents a novel graph-based misinformation detection method leveraging knowledge embeddings and introduces the CoVaxLies dataset for COVID-19 vaccine misinformation.

Findings

Graph link prediction outperforms classification methods

CoVaxLies dataset effectively captures misinformation topics

Knowledge embedding methods improve detection accuracy

Abstract

Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.