On the Study of Diurnal Urban Routines on Twitter

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.  Social media activity in different geographic regions expose a varied set of temporal patterns. In particular, Social Awareness Streams (SAS) (Naaman, Boase, and Lai 2010), available from social media services such as Facebook, Twitter, FourSquare, Flickr, and others, allow users to post streams of lightweight content artifacts, from short status messages to links, pictures, and videos, in a highly connected social environment. The vast amounts of SAS data reflect, in new ways, people’s attitudes, attention, and interests, offering unique opportunities to understand and draw insights about social trends and habits. In this paper, we focus on characterizing social media patterns in different urban areas (US cities), with the goal of providing a framework for reasoning about activities and diurnal patterns in different cities. Using Twitter as a typical SAS, previous research studied specific temporal patterns that are similar across geographies, in particular in respect to expression of mood (Golder and Macy 2011; Dodds et al. 2011). We aim to provide insights for reasoning about diurnal patterns in different geographic (urban) areas that can be used in studying activity patterns in these areas, going beyond previous work that had mostly examined topical differences between posts in different geographic areas (Eisenstein et al. 2010; Hecht et al. 2011) or briefly examined broad diurnal differences (Cheng et al. 2011) in vol∗Amy and Sam were at Rutgers at the time of this work.  Developing such a framework presents a number of challenges, both technical and practical. First, SAS data (and in particular Twitter) has been shown to be quite noisy. Users of SAS post different type of content, from information and link sharing, to personal updates, to social interactions, and many others (Naaman, Boase, and Lai 2010). Can stable patterns be reliably extracted given this noisy environment? Second, reliably extracting the location associated with Twitter content is still an open problem, as we discuss below. Finally, Twitter content volume shifts over time as more users join the service, and fluctuates widely in response to breaking events and other happenings, from Valentine’s Day to the news about Bin Laden’s capture and demise. Such temporal volume fluctuations might distort otherwise stable patterns and make them difficult to extract. In this paper, therefore, we report on a study that extracts and reasons about stable temporal patterns from Twitter data. In particular, we: 1) use large scale data with manual coding to get a wide sample of tweets for different cities; 2) study within-day and across-day variability of patterns in cities; and 3) reason about differences between cities with respect to overall patterns as well as individual ones. Related Work Broadly speaking, this work is informed by two key areas of related work: the use of new technologies and data sources for urban studies, and studies of social media to extract “real world” insights, or temporal dynamics. Here we broadly address these areas, before discussing other recent research that directly informed our work. The related research area sometimes dubbed “urban sensing” (Cuff, Hansen, and Kang 2008) analyzes various new datasets to understand the dynamics and patterns of urban activity. Most prominently, mobile phone data, mainly proprietary data from wireless carriers (e.g., calls made and positioning data) help expose travel patterns and broad spatio-temporal dynamics, e.g., in (Gonzalez, Hidalgo, and Barabasi 2008). Social media was also used to augment 258 Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media