Abstract
Topic modelling is a well-studied field that aims to identify topics from traditional documents such as news articles and reports. More recently, Latent Dirichlet Allocation (LDA) and its variants, have been applied on social media platforms to model and study topics relating to sports, politics and companies. While these applications were able to successfully identify the general topics, we posit that standard LDA can be augmented with spatial and temporal considerations based on the geo-coordinates and timestamps of social media posts. Towards this effort, we propose a spatial and temporal variant of LDA to better detect more specific topics, such as a particular art exhibit held at a museum or a security incident happening on a particular day. We validate our approach on a Twitter dataset and find that the detected topics are well-aligned to real-life events happening on the specific days and locations.