A1 Journal article (refereed)
Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model (2022)

Liu, P., Koivisto, S., Hiippala, T., van der Lijn, C., Väisänen, T., Nurmi, M., Toivonen, T., Vehkakoski, K., Pyykönen, J., Virmasalo, I., Simula, M., Hasanen, E., Salmikangas, A.-K., & Muukkonen, P. (2022). Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model. Journal of Spatial Information Science, (24), 31-61. https://doi.org/10.5311/JOSIS.2022.24.167

JYU authors or editors

Publication details

All authors or editors: Liu, Pengyuan; Koivisto, Sonja; Hiippala, Tuomo; van der Lijn, Charlotte; Väisänen, Tuomas; Nurmi, Marisofia; Toivonen, Tuuli; Vehkakoski, Kirsi; Pyykönen, Janne; Virmasalo, Ilkka; et al.

Journal or series: Journal of Spatial Information Science

eISSN: 1948-660X

Publication year: 2022

Publication date: 20/06/2022

Issue number: 24

Pages range: 31-61

Publisher: National Center for Geographic Information and Analysis

Publication country: United States

Publication language: English

DOI: https://doi.org/10.5311/JOSIS.2022.24.167

Persistent website address: http://204.48.17.207/index.php/josis/article/view/167

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/82535

Abstract

Sport and exercise contribute to health and well-being in cities. While previous research has mainly focused on activities at specific locations such as sport facilities, "informal sport" that occur at arbitrary locations across the city have been largely neglected. Such activities are more challenging to observe, but this challenge may be addressed using data collected from social media platforms, because social media users regularly generate content related to sports and exercise at given locations. This allows studying all sport, including those "informal sport" which are at arbitrary locations, to better understand sports and exercise-related activities in cities. However, user-generated geographical information available on social media platforms is becoming scarcer and coarser. This places increased emphasis on extracting location information from free-form text content on social media, which is complicated by multilingualism and informal language. To support this effort, this article presents an end-to-end deep learning-based bilingual toponym recognition model for extracting location information from social media content related to sports and exercise. We show that our approach outperforms five state-of-the-art deep learning and machine learning models. We further demonstrate how our model can be deployed in a geoparsing framework to support city planners in promoting healthy and active lifestyles.

Keywords: physical training; sports grounds and physical exercise facilities; urban geography; geographic information; text mining; place names; social media; machine learning; deep learning

Free keywords: digital geography; deep learning; geoparsing; georeferencing; social media; sports geography; toponym recognition

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1