Geo-Information Harvesting from Social Media Data

• URL: http://arxiv.org/abs/2211.00543v1
• Date: Tue, 1 Nov 2022 15:47:18 GMT
• Title: Geo-Information Harvesting from Social Media Data
• Authors: Xiao Xiang Zhu, Yuanyuan Wang, Mrinalini Kochupillai, Martin Werner, Matthias H\"aberle, Eike Jens Hoffmann, Hannes Taubenb\"ock, Devis Tuia, Alex Levering, Nathan Jacobs, Anna Kruspe, Karam Abdulahhad
• Abstract summary: Massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream. We address key aspects in the field, including data availability, analysis-ready data preparation and data management. We present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications.
• Score: 22.061969480185482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.

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