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        <title>A Multi-Baseline Framework for Ranking Global Event Significance Using Google Trends and Large Language Models</title>
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            <namePart type="given">Ivelina</namePart>
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    <abstract>Determining global event significance lacks standardized metrics for quantifying worldwide impact. While Google Trends has demonstrated utility in domain-specific studies, its application to global event ranking remains limited. This paper presents a framework combining Google Trends data with large language models for automated global event ranking. This study leverages Command R+ and Llama 3.3-70B-Instruct to generate contextually relevant event keywords and establishes significance through comparative search volume analysis against baseline reference terms, incorporating temporal weighting mechanisms to address chronological biases. The proposed methodology identified globally significant events across technology, health, sports, and natural disasters from a dataset of 1,094 events (2020-2024) extracted from Wikipedia.</abstract>
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%0 Conference Proceedings
%T A Multi-Baseline Framework for Ranking Global Event Significance Using Google Trends and Large Language Models
%A Chen, Zenan
%Y Velichkov, Boris
%Y Nikolova-Koleva, Ivelina
%Y Slavcheva, Milena
%S Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F chen-2025-multi
%X Determining global event significance lacks standardized metrics for quantifying worldwide impact. While Google Trends has demonstrated utility in domain-specific studies, its application to global event ranking remains limited. This paper presents a framework combining Google Trends data with large language models for automated global event ranking. This study leverages Command R+ and Llama 3.3-70B-Instruct to generate contextually relevant event keywords and establishes significance through comparative search volume analysis against baseline reference terms, incorporating temporal weighting mechanisms to address chronological biases. The proposed methodology identified globally significant events across technology, health, sports, and natural disasters from a dataset of 1,094 events (2020-2024) extracted from Wikipedia.
%U https://aclanthology.org/2025.ranlp-stud.1/
%P 1-9

Markdown (Informal)

[A Multi-Baseline Framework for Ranking Global Event Significance Using Google Trends and Large Language Models](https://aclanthology.org/2025.ranlp-stud.1/) (Chen, RANLP 2025)

• A Multi-Baseline Framework for Ranking Global Event Significance Using Google Trends and Large Language Models (Chen, RANLP 2025)

ACL

• Zenan Chen. 2025. A Multi-Baseline Framework for Ranking Global Event Significance Using Google Trends and Large Language Models. In Proceedings of the 9th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing, pages 1–9, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.