A Bayesian Graphical Model to Discover Latent Events from Twitter

Abstract

Online social networks like Twitter and Facebook produce an overwhelming amount of information every day. However, research suggests that much of this content focuses on a reasonably sized set of ongoing events or topics that are both temporally and geographically situated. These patterns are especially observable when the data that is generated contains geospatial information, usually generated by a location enabled device such as a smartphone. In this paper, we consider a data set of 1.4 million geo-tagged tweets from a country during a large social movement, where social events and demonstrations occurred frequently. We use a probabilistic graphical model to discover these events within the data in a way that informs us of their spatial, temporal and topical focus. Quantitative analysis suggests that the streaming algorithm proposed in the paper uncovers both well-known events and lesser-known but important events that occurred within the timeframe of the dataset. In addition, the model can be used to predict the location and time of texts that do not have these pieces of information, which accounts for the much of the data on the web.