Big Data Social Networks and Human Behavior

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					                         Big Data, Social Networks, and Human Behavior



                                            Jukka-Pekka Onnela
                                             Harvard University


                                         Big Data for Development
                                 United Nations Headquarters, New York City
                                               July 10, 2012



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            Overview

             • Progress in science has always been driven by data
             • Explosion in the amount and type of data
             • “Big data” refers to large and complex data sets
                  • Often multidimensional, longitudinal, digitally generated
             • The big data phenomenon has its origin in Moore’s law:
                  • The number of transistors on integrated circuits doubles every 18 months
                  • Sensors are cheaper, smaller, everywhere
                  • Enhanced computational capacity




                                                                     http://en.wikipedia.org/wiki/Moore%27s_law

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                         http://www.boston.com/bigpicture/2008/08/the_large_hadron_collider.html

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            Large Hadron Collider (LCH)

             • Large Hadron Collider (LHC) at CERN is the biggest machine ever built
                  • Largest underground ring has a circumference of 27 kilometers (17 miles)
                  • 1232 dipole magnets, each 15 meters long weighing 30 tons
                  • Vacuum is 10 trillionth of an atmosphere
             • Experiments generate 100MB of data (particle trajectories) each second
             • Higgs boson




                                    http://www.runfam.com/2011/10/why-the-hare-may-never-beat-the-tortoise-zenos-paradox-the-paradox-of-motion/

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            Networks and mobile sensing

             • Mobile phones have been used in the past few years to study the structure
               of human social and communication networks

             • Networks consist of nodes (actors) and ties (interactions)




             • The field of research that studies networks, their structure and function, is
               called network science (in physics and mathematics) or social network
               analysis (in sociology and statistics)




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            Networks and mobile sensing

             • Network theory, when applied to social networks, has a simple premise

                         • People are connected, therefore our health is connected

                         • People are connected, therefore our economic wellbeing is connected

             • Mobile phones have enormous potential for the study of human social networks
               and human behavior “in vivo,” in a natural context outside laboratories

             • Social behavior has remained essentially unchanged for millennia, but now, for
               the first time, we have the opportunity to study it at large scale




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            Networks and mobile sensing

             • Besides communication, smartphones have sensors and computing capabilities

             • These possibilities have led to a new research field called mobile phone sensing

             • Mobile sensing has evolved in the past few years for several reasons

                         (1) Availability of cheap embedded sensors

                            • Gyroscope, compass, accelerometer, proximity sensor, ambient light
                              sensor, two cameras, microphone, GPS, WiFi, Bluetooth

                         (2) Smartphones are programmable

                         (3) Software can be easily distributed

                         (4) Significant computational power (phone & cloud)

             • Each phone can generate 1kB of data / second (conservative)




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                         http://desktopwallpaperdownload.files.wordpress.com/2012/02/network-space-lights-planets-high-wallpapers-full-hd.jpg

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            Networks and mobile sensing

             • Expect 6 billion phone subscriptions by the end of 2012

             • This results in 6 million MB / second, or 6 TB / second, of data

             • This is 60,000 more data than CERN generates (conservative)

             • Twofold opportunity:

                         • Use mobile phone sensing to learn about the individuals (nodes)

                         • Use mobile phone communication patterns and network theory to
                           learn about the structural connections between individuals (ties)




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            Phone calls and texts in a European network

                         • 20% market share
                         • 18 weeks (126 days)
                         • Private subscriptions
                         • N = 7M; L = 23M




                                         Animation by Mikko Kivelä, Aalto University


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            Tie strengths in social networks

              The weak ties hypothesis




       Mark Granovetter, The strength of weak ties, American Journal of Sociology 78, 1360, 1973




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            Tie strengths in social networks

             Revisiting the hypothesis with aggregated cell phone data

                 • Tie strength

                 • Fraction of friends in common


                         7 min

                                                  15 min   (3 calls)
                         5 min

                         3 min




       Onnela, Saramäki, Hyvönen, Szabó, Lazer, Kaski, Kertész, Barabási
       Structure and tie strengths in mobile communication networks, PNAS 104, 7332, 2007



Tuesday, July 10, 2012                                                                      13
            Tie strengths in social networks




       Onnela, Saramäki, Hyvönen, Szabó, Lazer, Kaski, Kertész, Barabási
       Structure and tie strengths in mobile communication networks, PNAS 104, 7332, 2007



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            Tie strengths in social networks




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            Tie strengths in social networks




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            Tie strengths in social networks




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            Tie strengths in social networks




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            Pulse of the nation: Mood from Twitter




       Understanding the Demographics of Twitter Users; A Mislove, S Lehmann, YY Ahn, JP Onnela, JN Rosenquist;
       Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, 2011

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                         Thank you




                         jponnela.com



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