Leveraging Large Language Models to Geolocate Linguistic Variations in Social Media Posts

Geolocalization of social media content is the task of determining the geographical location of a user based on textual data, that may show linguistic variations and informal language. In this project, we address the GeoLingIt challenge of geolocalizing tweets written in Italian by leveraging large...

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Bibliographic Details
Published inarXiv.org
Main Authors Savarro, Davide, Zago, Davide, Zoia, Stefano
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.07.2024
Subjects
Digital media
Geographical locations
Large language models
Linguistics
Social networks
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Summary:Geolocalization of social media content is the task of determining the geographical location of a user based on textual data, that may show linguistic variations and informal language. In this project, we address the GeoLingIt challenge of geolocalizing tweets written in Italian by leveraging large language models (LLMs). GeoLingIt requires the prediction of both the region and the precise coordinates of the tweet. Our approach involves fine-tuning pre-trained LLMs to simultaneously predict these geolocalization aspects. By integrating innovative methodologies, we enhance the models' ability to understand the nuances of Italian social media text to improve the state-of-the-art in this domain. This work is conducted as part of the Large Language Models course at the Bertinoro International Spring School 2024. We make our code publicly available on GitHub https://github.com/dawoz/geolingit-biss2024.
ISSN:2331-8422