XAIvsDisinfo: eXplainable AI Methods for Categorisation and Analysis of COVID-19 Vaccine Disinformation and Online Debates
Lead Research Organisation:
University of Sheffield
Department Name: Computer Science
Abstract
UK vaccination rates are in decline and experts believe that vaccine disinformation, widely spread
in social media, may be one of the reasons. Recent surveys have established that vaccine
disinformation is impacting negatively citizen trust in COVID-19 vaccination specifically. As a
response, the UK Government agreed with Twitter, Facebook, and YouTube measures to limit the
spread of disinformation. However, simply removing disinformation from platforms is not enough,
as the government also needs to monitor and respond to the concerns of vaccine hesitant citizens.
Moreover, manual detection and tracking of disinformation, as currently practiced by many
journalists, is infeasible, given the scale of social media.
XAIvsDinfo aims to address these gaps through novel research on explainable AI-based models for
large-scale analysis of vaccine disinformation. Specifically, vaccine disinformation will be classified
automatically into the six narrative types defined by First Draft. A second model will categorise
vaccine statements as pro-vaccine, anti-vaccine, vaccine-hesitant, or other.
We will investigate explainable machine learning approaches that are human interpretable: both
in detecting errors and weaknesses of the models and in providing human-readable explanations
of the models' decisions.
XAIvsDisinfo will also create two new multi-platform datasets and organise a new community
research challenge on cross-platform analysis of vaccine disinformation, as follow-up from our
RumourEval one.
Our XAI models and tools will be integrated into the open-source InVID-WeVerify plugin, for take
up by journalists and fact-checkers. The project outputs will also contribute to evidence-based
policy activities by the UK government on improving citizen perception of COVID-19 vaccines.
in social media, may be one of the reasons. Recent surveys have established that vaccine
disinformation is impacting negatively citizen trust in COVID-19 vaccination specifically. As a
response, the UK Government agreed with Twitter, Facebook, and YouTube measures to limit the
spread of disinformation. However, simply removing disinformation from platforms is not enough,
as the government also needs to monitor and respond to the concerns of vaccine hesitant citizens.
Moreover, manual detection and tracking of disinformation, as currently practiced by many
journalists, is infeasible, given the scale of social media.
XAIvsDinfo aims to address these gaps through novel research on explainable AI-based models for
large-scale analysis of vaccine disinformation. Specifically, vaccine disinformation will be classified
automatically into the six narrative types defined by First Draft. A second model will categorise
vaccine statements as pro-vaccine, anti-vaccine, vaccine-hesitant, or other.
We will investigate explainable machine learning approaches that are human interpretable: both
in detecting errors and weaknesses of the models and in providing human-readable explanations
of the models' decisions.
XAIvsDisinfo will also create two new multi-platform datasets and organise a new community
research challenge on cross-platform analysis of vaccine disinformation, as follow-up from our
RumourEval one.
Our XAI models and tools will be integrated into the open-source InVID-WeVerify plugin, for take
up by journalists and fact-checkers. The project outputs will also contribute to evidence-based
policy activities by the UK government on improving citizen perception of COVID-19 vaccines.
Organisations
Publications
Singh I
(2023)
UTDRM: unsupervised method for training debunked-narrative retrieval models
in EPJ Data Science
Li Y
(2023)
Classifying COVID-19 Vaccine Narratives
Description | We have studied the spread of vaccine narratives online, including vaccine hesitancy. The research is still ongoing and involved journalists and data scientists, as well as AI researchers developing intelligent methods for detection and classification of vaccine hesitant and anti-vaccine posts, as well as categorisation of vaccine narratives into 6 topical categories. |
Exploitation Route | The award is still ongoing, so this will be addressed at a later stage. |
Sectors | Digital/Communication/Information Technologies (including Software),Government, Democracy and Justice |
Description | Provided input to DCMS to help shape up the Online Harms bill. Used by journalists at First Draft for their work on vaccine narratives. |
First Year Of Impact | 2021 |
Sector | Digital/Communication/Information Technologies (including Software),Government, Democracy and Justice |
Impact Types | Societal,Policy & public services |
Description | Participation in DCMS task force and virtual round table on tackling COVID-19 and vaccine misinformation |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | Learnings from the task force have been used to inform the Online Harms bill going through Parliament. |
URL | https://www.gov.uk/government/news/social-media-giants-agree-package-of-measures-with-uk-government-... |
Description | Participation in the DCMS College of Experts |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | Research on online disinformation and online abuse has played a key role in informing the latest Online Harms bill which is going to be put through Parliament. |
Description | vera.ai: Verification Assisted by Artificial Intelligence |
Amount | £900,000 (GBP) |
Funding ID | https://www.veraai.eu/home |
Organisation | Innovate UK |
Sector | Public |
Country | United Kingdom |
Start | 09/2022 |
End | 09/2025 |
Title | COVID-19 Vaccine Narrative Categoriser |
Description | A machine learning classifier trained to categorise COVID-19 vaccine text into 6 categories. These are: Liberty/Freedom Development Provision and Access Safety Efficacy and Necessity Politics and Economics Conspiracy Morality Religiosity and Ethics |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Used by First Draft in their research on vaccine misinformation. |
URL | https://cloud.gate.ac.uk/shopfront/displayItem/covid19-vaccine |
Title | Text Classification Web Annotation Tool |
Description | A web application which enables a team of researchers to jointly annotate vaccine narratives for vaccine hesitancy and narrative category, for the purpose of training machine learning models. |
Type Of Technology | Webtool/Application |
Year Produced | 2021 |
Impact | The tool is still being developed and the award plans to use to for the development and release of two datasets. |