VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 6967–6975 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 6967 VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines Maxwell A. Weinzierl, Sanda M. Harabagiu Human Language Technology Research Institute, Department of Computer Science, The University of Texas at Dallas 800 W. Campbell Rd., Richardson, TX 75080 {maxwell.weinzierl, sanda}@utdallas.edu Abstract Billions of COVID-19 vaccines have been administered, but many remain hesitant. Misinformation about the COVID-19 vaccines and other vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. The ability to automatically recognize misinformation targeting vaccines on Twitter depends on the availability of data resources. In this paper we present VACCINELIES, a large collection of tweets propagating misinformation about two vaccines: the COVID-19 vaccines and the Human Papillomavirus (HPV) vaccines. Misinformation targets are organized in vaccine-specific taxonomies, which reveal the misinformation themes and concerns. The ontological commitments of the misinformation taxonomies provide an understanding of which misinformation themes and concerns dominate the discourse about the two vaccines covered in VACCINELIES. The organization into training, testing and development sets of VACCINELIES invites the development of novel supervised methods for detecting misinformation on Twitter and identifying the stance towards it. Furthermore, VACCINELIES can be a stepping stone for the development of datasets focusing on misinformation targeting additional vaccines. Keywords: COVID-19, HPV, vaccine, misinformation, Twitter, social media, stance. 1. Introduction Misinformation spreading, especially on social me- dia, is believed to be responsible for vaccine hesitancy (Kouzy et al., 2020). It is imperative for public health practitioners to know what misinformation is spreading as well as who is adopting or rejecting it, such that in- terventions can be tailored appropriately. Public health messaging approaches could not only inoculate against misinformation, but also effectively reach social me- dia users with the aim of shifting or bolstering vaccine attitudes. However, developing natural language pro- cessing methods targeting the identification of misin- formation about vaccines in social media postings suf- fers from the lack of language resources where vaccine misinformation annotations are available. When misinformation was cast as rumor detection, sev- eral well-known benchmark datasets for misinforma- tion detection on Twitter became available. For exam- ple, the Twitter15 (Ma et al., 2016) and Twitter16 (Ma et al., 2017) datasets consist of a collection of tweets annotated as true rumors, false rumors, unverified ru- mors or non-rumors. Unfortunately, they did not cover any vaccine misinformation. Similarly, the PHEME dataset (Zubiaga et al., 2016) consists of Twitter con- versation threads making a true and false claim, and a series of replies, but none of the conversations focused on vaccination. COVIDLIES (Hossain et al., 2020) is a dataset generated from 86 known misconceptions about COVID-19, for which tweets that evoke the miscon- ceptions were retrieved and annotated with their stance towards the misconceptions. Inspired by COVIDLIES, we have created VACCINELIES1, a dataset which ad- 1github.com/Supermaxman/vaccine-lies dresses misinformation about two different vaccines: the COVID-19 …

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