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Open Access
Peer-reviewed
Research Article
Large language models can consistently generate high-quality content for election disinformation operations
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Angus R Williams ,
Contributed equally to this work with: Angus R Williams, Liam Burke-Moore, Ryan Sze-Yin Chan, Florence E. Enock, Federico Nanni, Tvesha Sippy
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing
* E-mail: arwilliams@turing.ac.uk
Affiliation Public Policy, The Alan Turing Institute, London, United Kingdom
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Liam Burke-Moore ,
Contributed equally to this work with: Angus R Williams, Liam Burke-Moore, Ryan Sze-Yin Chan, Florence E. Enock, Federico Nanni, Tvesha Sippy
Roles Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing
Affiliation Public Policy, The Alan Turing Institute, London, United Kingdom
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Ryan Sze-Yin Chan ,
Contributed equally to this work with: Angus R Williams, Liam Burke-Moore, Ryan Sze-Yin Chan, Florence E. Enock, Federico Nanni, Tvesha Sippy
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Research Engineering Group, The Alan Turing Institute, London, United Kingdom
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Florence E. Enock ,
Contributed equally to this work with: Angus R Williams, Liam Burke-Moore, Ryan Sze-Yin Chan, Florence E. Enock, Federico Nanni, Tvesha Sippy
Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision
Affiliation Public Policy, The Alan Turing Institute, London, United Kingdom
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Federico Nanni ,
Contributed equally to this work with: Angus R Williams, Liam Burke-Moore, Ryan Sze-Yin Chan, Florence E. Enock, Federico Nanni, Tvesha Sippy
Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Research Engineering Group, The Alan Turing Institute, London, United Kingdom
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Tvesha Sippy ,
Contributed equally to this work with: Angus R Williams, Liam Burke-Moore, Ryan Sze-Yin Chan, Florence E. Enock, Federico Nanni, Tvesha Sippy
Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing
Affiliation Public Policy, The Alan Turing Institute, London, United Kingdom
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Yi-Ling Chung,
Roles Methodology, Writing – review & editing
Affiliation Public Policy, The Alan Turing Institute, London, United Kingdom
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Evelina Gabasova,
Roles Conceptualization, Project administration, Resources, Software, Supervision
Affiliation Research Engineering Group, The Alan Turing Institute, London, United Kingdom
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Kobi Hackenburg,
Roles Funding acquisition, Writing – review & editing
Affiliations Public Policy, The Alan Turing Institute, London, United Kingdom, Oxford Internet Institute, University of Oxford, Oxford, United Kingdom
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Jonathan Bright
Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing
Affiliation Public Policy, The Alan Turing Institute, London, United Kingdom
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Large language models can consistently generate high-quality content for election disinformation operations
- Angus R Williams,
- Liam Burke-Moore,
- Ryan Sze-Yin Chan,
- Florence E. Enock,
- Federico Nanni,
- Tvesha Sippy,
- Yi-Ling Chung,
- Evelina Gabasova,
- Kobi Hackenburg,
- Jonathan Bright
- Published: March 17, 2025
- https://doi.org/10.1371/journal.pone.0317421