On the study of diurnal urban routines on twitter

Abstract

Social media activity in different geographic regions can expose a varied set of temporal patterns. We study and characterize diurnal patterns in social media data for different urban areas, with the goal of providing context and framing for reasoning about such patterns at different scales. Using one of the largest datasets to date of Twitter content associated with different locations, we examine within-day variability and across-day variability of diurnal keyword patterns for different locations.We show that only a few cities currently provide the magnitude of content needed to support such acrossday variability analysis for more than a few keywords. Nevertheless, within-day diurnal variability can help in comparing activities and finding similarities between cities. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.