Social media are transforming global communication and coordination and provide unprecedented opportunities for studying socio-technical domains. Here we study global dynamical patterns of communication on Twitter across many scales. Underlying the observed patterns is both the diurnal rotation of the earth, day and night, and the synchrony required for contingency of actions between individuals. We find that urban areas show a cyclic contraction and expansion that resembles heartbeats linked to social rather than natural cycles. Different urban areas have characteristic signatures of daily collective activities. We show that the differences detected are consistent with a new emergent global synchrony that couples behavior in distant regions across the world. Although local synchrony is the major force that shapes the collective behavior in cities, a larger-scale synchronization is beginning to occur.
CAMBRIDGE (February 28, 2016) — Human activity, whether commercial or social, contains patterns and moments of synchronicity. In recent years, social media like Twitter has provided an unprecedented volume of data on the daily activities of humans all over the world. Observing this activity on the scale of a city, a continent, or the globe reveals the patterns. In a paper published by the Journal of the Royal Society, researchers at the New England Complex Systems Institute (NECSI) have observed a new pattern of synchronized activity: a simultaneous peak of Twitter activity stretching across half the planet, from Europe and Africa to Asia and Oceania.
Everyone has their daily routine, which for many people now includes tweeting. NECSI researchers observed over 500 million tweets to obtain the aggregate synchronizations created by everyone’s routine.
When viewing the tweets of a single city, human activity resembles a heart beat: a strong peak of activity coinciding with movement contracting into the city center for the work day, followed by a secondary peak of activity representing afterwork social and commercial activity, and ending in a period of low activity and dispersal away from the city center as people return to homes to sleep. As NECSI watched this daily pattern over the course of the year, they found it had more to do with the demands of work schedules than the natural cycles of night and day, changing little in comparison to the shortening and lengthening days of the year.
Using tweets, NECSI took the pulse of 52 metropolitan areas all over the world. While the heart beat pattern was observed in each city, some locations had denser or more disperse work and home neighborhoods. The relevant size and timing of peaks of activity also varied. Perhaps not surprisingly, cities in the same longitude and timezone tend to have similar patterns. However, NECSI observed a new pattern of synchronized activity falling across longitudes 0 to 180: Europe’s morning peak of Twitter activity coincides with Asia’s large peak of afternoon activity.
Global patterns of human activity, as observed by NECSI through tweets, have synchronized across the entire Eurasian landmass. This pattern is formed of commercial as well as social behavior. It represents a global interchange of ideas and information, a new level of interconnectedness in our increasingly complex world.
24 Hours of Global Twitter Activity. Click image to download one month time-lapse video.
Spatio-temporal dynamics of Twitter activity in urban areas. Activity during an average day according to UTC is shown for specified cities. Colors indicate the normalized excess of activity from the average value at that location.
Global Twitter Activity. Background map: Twitter activity in each 0.25◦x0.25◦ geographic area (base-10 log scale at lower right). Rectangular insets: Average week of Twitter activity of selected cities in Universal Time (UTC) after subtracting the mean and normalizing by the standard deviation. Square insets: Low (blue) and high (red) points of Twitter activity of several urban areas compared to daily sunlight periods (yellow) during the nine month observation period (scales on lower left shown for Santiago are the same for all cities).
A. Temporal dynamics of an average week of Twitter activity by longitude. The vertical axis represents time (increasing from top to bottom) and the horizontal axis represents longitude. Significant cities are indicated at the top. Diagonal dashed lines show peaks of activity (black) and inactivity (white) tracking the time of day. Horizontal line (solid black) indicates synchronous linked activity across Europe, Asia, Africa and Oceania (scale on right). B. Urban correlation network across a time window from 3pm to 3am UTC. C. Like B but across a time window from 1am to 1pm UTC. Colors indicate the results of a clustering algorithm, after aggregating the networks over time.
Spatio-temporal dynamics of Twitter activity in urban areas. Each row shows activity during an average day according to UTC time for the specified city. Colors indicate the normalized excess of activity from the average value at that location (scale shown in figure).
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