Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model

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http://hdl.handle.net/10138/346273

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Liu , P , Koivisto , S M , Hiippala , T , Van der Lijn , C J C , Väisänen , T L A , Nurmi , M K , 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 , no. 24 , pp. 31-61 . https://doi.org/10.5311/JOSIS.2022.24.167

Title: Extracting locations from sport and exercise-related social media messages using a neural network-based bilingual toponym recognition model
Author: Liu, Pengyuan; Koivisto, Sonja Maria; Hiippala, Tuomo; Van der Lijn, Charlotte Jacoba Cornelia; Väisänen, Tuomas Lauri Aleksanteri; Nurmi, Marisofia Kaarina; Toivonen, Tuuli; Vehkakoski, Kirsi; Pyykönen, Janne; Virmasalo, Ilkka; Simula, Mikko; Hasanen, Elina; Salmikangas, Anna-Katriina; Muukkonen, Petteri
Contributor organization: Helsinki Institute of Urban and Regional Studies (Urbaria)
Digital Geography Lab
Department of Geosciences and Geography
Department of Languages
Helsinki Institute of Sustainability Science (HELSUS)
Helsinki Inequality Initiative (INEQ)
Teachers' Academy
Earth Change Observation Laboratory (ECHOLAB)
Date: 2022-06
Language: eng
Number of pages: 31
Belongs to series: Journal of Spatial Information Science
ISSN: 1948-660X
DOI: https://doi.org/10.5311/JOSIS.2022.24.167
URI: http://hdl.handle.net/10138/346273
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.
Subject: ACCESSIBILITY
GEOGRAPHY
PERSPECTIVES
PLACES
SPACE
deep learning
digital geography
geoparsing
georeferencing
social media
sports geography
toponym recognition
1171 Geosciences
519 Social and economic geography
518 Media and communications
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion
Funder: Ympäristöministeriön asettama lähiöohjelma
Emil Aaltosen Säätiö
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