On the Study of Diurnal Urban Routines on Twitter

Mor Naaman · Amy Xian Zhang · Samuel Brody · Gilad Lotan

36 citations0/0 files storedProceedings of the International AAAI Conference on Web and Social Media

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Human Mobility and Location-Based Analysis Smart Cities and Technologies Computational and Text Analysis Methods

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 across-day variability analysis for more than a few keywords. Nevertheless, within-day diurnal variability can help in comparing activities and finding similarities between cities.

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References

2008Understanding individual human mobility patterns 2010You are where you tweet 2011Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter 2009Mapping the world's photos 2021Exploring Millions of Footprints in Location Sharing Services 2009Understanding individual human mobility patterns 2011Tweets from Justin Bieber's heart 2008Leveraging explicitly disclosed location information to understand tourist dynamics: a case study

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