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        <title>A Thesis Proposal ClaimInspector Framework: A Hybrid Approach to Data Annotation using Fact-Checked Claims and LLMs</title>
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    <abstract>This thesis explores the challenges and limitations encountered in automated fact-checking processes, with a specific emphasis on data annotation in the context of misinformation. Despite the widespread presence of misinformation in multiple formats and across various channels, current efforts concentrate narrowly on textual claims sourced mainly from Twitter, resulting in datasets with considerably limited scope. Furthermore, the absence of automated control measures, coupled with the reliance on human annotation, which is very limited, increases the risk of noisy data within these datasets. This thesis proposal examines the existing methods, elucidates their limitations and explores the potential integration of claim detection subtasks and Large Language Models to mitigate these issues. It introduces ClaimInspector, a novel framework designed for a systemic collection of multimodal data from the internet. By implementing this framework, this thesis will propose a dataset comprising fact-checks alongside the corresponding claims made by politicians. Overall, this thesis aims to enhance the accuracy and efficiency of annotation processes, thereby contributing to automated fact-checking efforts.</abstract>
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%0 Conference Proceedings
%T A Thesis Proposal ClaimInspector Framework: A Hybrid Approach to Data Annotation using Fact-Checked Claims and LLMs
%A Bozkurt, Basak
%Y Falk, Neele
%Y Papi, Sara
%Y Zhang, Mike
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F bozkurt-2024-thesis
%X This thesis explores the challenges and limitations encountered in automated fact-checking processes, with a specific emphasis on data annotation in the context of misinformation. Despite the widespread presence of misinformation in multiple formats and across various channels, current efforts concentrate narrowly on textual claims sourced mainly from Twitter, resulting in datasets with considerably limited scope. Furthermore, the absence of automated control measures, coupled with the reliance on human annotation, which is very limited, increases the risk of noisy data within these datasets. This thesis proposal examines the existing methods, elucidates their limitations and explores the potential integration of claim detection subtasks and Large Language Models to mitigate these issues. It introduces ClaimInspector, a novel framework designed for a systemic collection of multimodal data from the internet. By implementing this framework, this thesis will propose a dataset comprising fact-checks alongside the corresponding claims made by politicians. Overall, this thesis aims to enhance the accuracy and efficiency of annotation processes, thereby contributing to automated fact-checking efforts.
%U https://aclanthology.org/2024.eacl-srw.16
%P 215-224

Markdown (Informal)

[A Thesis Proposal ClaimInspector Framework: A Hybrid Approach to Data Annotation using Fact-Checked Claims and LLMs](https://aclanthology.org/2024.eacl-srw.16) (Bozkurt, EACL 2024)

• A Thesis Proposal ClaimInspector Framework: A Hybrid Approach to Data Annotation using Fact-Checked Claims and LLMs (Bozkurt, EACL 2024)

ACL

• Basak Bozkurt. 2024. A Thesis Proposal ClaimInspector Framework: A Hybrid Approach to Data Annotation using Fact-Checked Claims and LLMs. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 215–224, St. Julian’s, Malta. Association for Computational Linguistics.