Data and code used for the work entitled "Content-location relationships: a framework to explore correlations between space-based and place-based user-generated content"

The use of social media and location-based networks through GPS-enabled devices provides geospatial data for a plethora of applications in urban studies. However, the extent to which information found in geo-tagged social media activity corresponds to the spatial context is still a topic of debate. In this article, we developed a framework aimed at retrieving the thematic and spatial relationships between content originated from space-based (Twitter) and place-based (Google Places and OSM) sources of geographic user-generated content based on topics identified by the embedding-based BERTopic model. The contribution of the framework lies on the combination of methods that were selected to improve previous works focused on content-location relationships. Using the city of Lisbon (Portugal) to test our methodology, we first applied the embedding-based topic model to aggregated textual data coming from each source. Results of the analysis evidenced the complexity of content-location relationships, which are mostly based on thematic profiles. Nonetheless, the framework can be employed in other cities and extended with other metrics to enrich the research aimed at exploring the correlation between online discourse and geography.

借助具备GPS功能的设备通过社交媒体和基于位置的社交网络，为城市研究领域的众多应用提供了地理空间数据。然而，地理标记的社交媒体活动中所发现的信息与空间背景之间的对应程度，仍然是一个颇具争议的话题。在本文中，我们构建了一个旨在从基于空间（Twitter）和基于地点（Google Places和OSM）的地理用户生成内容来源中检索内容与主题之间关系以及空间关系的框架。该框架的贡献在于所选择方法的组合，这些方法旨在改进以往专注于内容与位置关系的研究工作。以葡萄牙的里斯本市为测试对象，我们首先应用基于嵌入的BERTopic模型对来自每个来源的汇总文本数据进行了主题建模。分析结果证实了内容与位置关系之复杂，这些关系主要基于主题档案。尽管如此，该框架仍可应用于其他城市，并通过其他指标扩展，以丰富旨在探索在线话语与地理之间相关性的研究。