Place as Topics: Analysis of Spatial and Temporal Evolution of Topics from Social Networks Data

Geography in a commonsense way is about place. Place is a term used to describe the meaning that humans give to a location. Characterising a location as a place requires a huge amount of time to collect and analyse data. Furthermore, a place definition associated to a location can become rapidly obsolete. Nowadays, social networks and social media became very popular. People on social networks act like social sensors, reporting information about society, politics, economics, etc. Thus, many researchers have focused on the analysis of posts, combining them together with algorithms or extracting their meaning, keywords or users’ interests. In this paper, I will describe my research project, a visual framework that aims to simplify the process of place definition using topics generated from the application of Blei et al.’s Latent Dirichlet Allocation (Blei et al., 2003) on geo-referenced social networks data. My main assumption is that topics allow to capture the sense of place shared by social sensors. The framework will allow users to be not overwhelmed by the large amount of time and data required to understand and define places.