Geolingual Studies: Combining linguistics, remote sensing, and digital humanities to assess the interrelation of physical and social spaces

Kurzfassung

The last decades have seen an enormous increase of digital text data from Internet sources such as social media platforms, blogs, web forums, and web news. These data contain rich information about people’s perceptions, emotions, and opinions, as well as their activities and relationships. They are used in a large variety of fields, for instance, to assess human perception of their environment or gain situational awareness in disaster response (Z. Wang et al., 2019; Zhu et al., 2022). Some of these data also contain information on the geolocation of the messages, either in an explicit form (e.g., by using geotags) or in an implicit form (i.e., by mentioning locations in the texts), so that these messages can be located via geoparsing methods (Middleton et al., 2018). Therefore, the texts can be analyzed spatially e.g., by means of geostatistics or point-pattern analyses (Cressie, 2015). Additionally, timestamps included in these data allow to assess temporal trends, which can be combined with the geolocation to assess human mobility (Gonzalez et al., 2008). Using geographic location as a link, the content of the messages can be related to a wealth of geodata from other sources, such as volunteered geographic information (Goodchild, 2007), the internet of things (Kamilaris & Ostermann, 2018), or remote sensing imagery (H. Wang et al., 2018; Taubenböck et al., 2018). Combining such heterogeneous datasets offers new insights into how physical space and socially constructed space, i.e. place, interact. We call this new approach 'Geolingual Studies' (GLS), which integrates methods from digital humanities with those from remote sensing and linguistics, especially sociolinguistics, corpus-linguistics and (critical) discourse analysis, to investigate the relationship between physical space and place. Here we present two studies to illustrate the opportunities and challenges of the GLS framework: Firstly, we show how social media data and remote sensing data can be combined and contrasted to assess digital imbalances between new and old urban spaces. For 135 settlements across Africa, we used satellite imagery to map the growth of each settlement’s built-up area over time. This enabled us to compare the Tweet density between older and younger parts of the settlements. Results confirm the existence of a digital disparity between older and newer settlement areas that we found to be related, in complex ways, to settlement structure and the geographic setting. The other case study features an additional layer of analysis by integrating a linguistic analysis of the textual data in an application in a humanitarian crisis setting, i.e. the war in Ukraine. We included mobility information at user-level which enabled us to identify migrant flows and the main needs and interests of migrants across space and migration stages, based on geotagged Twitter data (Lemoine-Rodríguez et al., 2024). In this example, we show how topics can be identified across languages and characterized regarding the expressed opinions using a combination of natural language processing and qualitative analysis. The results show that the topics discussed by migrants on social media shifted depending on their migration stage (i.e., before leaving, after leaving, or after returning to Ukraine), and that the language used to communicate varied depending on the topic and the targeted audience. Challenges lie in the comparatively small proportion of text content which can be reliably geolocated (Zhu et al., 2022), the representativeness of the userbase (Lemoine-Rodriguez et al., 2024), the protection of users’ (geo-)privacy (Kounadi & Resch, 2018), and questions of ethics in this domain (Kochupillai et al., 2022). Additionally, as the example of Twitter demonstrates, the stability of data sources cannot be taken for granted (Davidson et al., 2023). However, many of the challenges associated with social media data can be effectively mitigated through technical methods. Furthermore, the comparison of results derived from social media with other datasets (e.g., official statistics) allows to confirm the plausibility of the insights derived from such data (Lemoine-Rodriguez et al., 2024). The benefits of social media data for research are substantial, since it represents a rich data source for research as well as for decision-makers e.g., in crisis response situations, providing first-hand information in real-time of various facets of human behavior, including needs, opinions, interests, sentiments, and in some cases, mobility (Hübl et al., 2017; Mast et al., 2024; Zhu et al., 2022). In this sense, the combination of insights derived from text data and traditional data sources has great potential to improve our understanding of society. Thus, the combination of geographic and linguistic approaches can help to assess social behavior in a more holistic manner than any of these disciplines alone. These insights can be useful for psychologists, urban planners, or sociologists, and contribute meaningfully to several research disciplines, including the Spatial Humanities.