Exploring geo-tagging behaviour in social media data through structural topic modelling and geographically weighted regression

In this study, Tweets were used as inputs for structural topic modelling (STM) to detect what topics were expressed by social media users. The STM identified 20 topics ranging from daily life to professional services. The results revealed how topic words were associated with geo-information versus non-geo-information in their posts. Additionally, geographically weighted regression (GWR) was used to investigate how geo-topics were spatially associated with land-use types, the results identified the specific areas where the geo-topics were positively correlated with different land-use categories.To avoid privacy concerns and to follow the terms of Twitter/X API, the raw individual Tweets are not published. The API terms here (https://developer.x.com/en/more/developer-terms/agreement-and-policy).