Minimum Collection Period for Viable Population Estimation from Social Media

Using volunteered geographic information for population estimation has shown promise in the fields of urban planning, emergency response and disaster recovery. A high volume of geospatially enabled Tweets can be leveraged to create population curves and/or heatmaps delineated by day of week and hour of day. When making these estimations, it is critical to have adequate data, or the confidence of the estimations will be low. This is especially pertinent to disaster response, where Tweet collection for a new city/town/locale may need to be rapidly deployed. Using previously leveraged data removal methods, temporal data quantity is explored using sets of data from increasingly longer collection periods. When generating these estimates, it is also necessary to identify and mitigate data from automated Twitter bots. This work examines the integration of a modern, web services based, Twitter bot assessment algorithm, executes data removal experiments on collected data, describes the technical architecture, and discusses results/follow-on work.