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TU Graz - Knowledge Management Institute




          Understanding co-evolution of social and
               content networks on Twitter

                     Philipp Singer, Claudia Wagner, Markus Strohmaier

                      Knowledge Management Institute and Know Center
                           Graz University of Technology, Austria



 Philipp Singer                            17.04.2012
                                                                         1
TU Graz - Knowledge Management Institute




                                           Motivation
             Social media applications allow users to share
              content and sozialize
                     Many social links and a lot of content


             Value of social media application depends on how it
              is used – i.e., activity of users

             Which factors impact users‘ content-related activities
              (e.g., hashtagging or link usage) and users‘ social
              activities (i.e., following)?


 Philipp Singer                                17.04.2012
                                                                       2
TU Graz - Knowledge Management Institute




                                                Aim
             Explore bi-directional longitudinal influence patterns
              between social and content properties


                                       ?
                                           ?
             Sample research questions
                    Does growth of a user's followers increase the number of authored
                     tweets?
                    Does an increase of used URLs of users also increases their usage
                     of hashtags?
                    …

 Philipp Singer                                17.04.2012
                                                                                         3
TU Graz - Knowledge Management Institute




                                                        ?

                                           ?                        ?

                                               ?

                                               ?                ?
                                       ?

 Philipp Singer                                    17.04.2012
                                                                        4
TU Graz - Knowledge Management Institute




                                           Experimental Setup
            • Twitter Dataset
                    1,500 random users chosen from public timeline
                    30 days time period (15.03.2011 – 14.04.2011)
                    Measure users’ social and content-related activities daily


             Use social and content properties to describe and
              abstract those activities

             Approach
                    Monitor and analyze how users‘ social activities and content-related
                     activities (and related outcomes) co-evole over time (Wang and
                     Groth 2010)

 Philipp Singer                                  17.04.2012
                                                                                            5
TU Graz - Knowledge Management Institute




                                           Methodology
             Multilevel autoregressive modeling
             Predict future outputs based on past outputs




             Variables are measured at different levels
             Coefficients can vary from user to user
             Overall coefficients determine influence between
              properties over time


 Philipp Singer                               17.04.2012
                                                                 6
TU Graz - Knowledge Management Institute


                  Does growth of a user's followers increase the
                           number of authored tweets?




                                           ?




 Philipp Singer                                17.04.2012
                                                                   7
TU Graz - Knowledge Management Institute


                    Does an increase of used URLs of users also
                         increases their usage of hashtags?




                                           ?


 Philipp Singer                                17.04.2012
                                                                  8
TU Graz - Knowledge Management Institute




                                                        ?

                                           ?                        ?

                                               ?

                                               ?                ?
                                       ?

 Philipp Singer                                    17.04.2012
                                                                        9
TU Graz - Knowledge Management Institute




                                           Conclusions
             Driven by social factors

             Attention of other users motivates individuals

             Social media hosts can adopt and use these
              techniques




 Philipp Singer                               17.04.2012
                                                               10
TU Graz - Knowledge Management Institute




                                    Limitations & Next Steps
             One small random Twitter dataset
              Apply techniques to further, larger datasets

             Limited to some chosen properties
              Produce different networks
                    Hashtag co-occurrence
                    Similarity networks


             Try different model approaches
             Analyze interface changes (e.g., recommender)

 Philipp Singer                              17.04.2012
                                                               11
TU Graz - Knowledge Management Institute




                                 Social properties influence content properties

                                              but not vice versa!



Thanks for your attention!

  @ph_singer
  @clauwa
  @mstrohm

  philipp.singer@tugraz.at
 Philipp Singer                                     17.04.2012
                                                                                  12

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Understanding co-evolution of social and content networks on Twitter

  • 1. TU Graz - Knowledge Management Institute Understanding co-evolution of social and content networks on Twitter Philipp Singer, Claudia Wagner, Markus Strohmaier Knowledge Management Institute and Know Center Graz University of Technology, Austria Philipp Singer 17.04.2012 1
  • 2. TU Graz - Knowledge Management Institute Motivation  Social media applications allow users to share content and sozialize   Many social links and a lot of content  Value of social media application depends on how it is used – i.e., activity of users  Which factors impact users‘ content-related activities (e.g., hashtagging or link usage) and users‘ social activities (i.e., following)? Philipp Singer 17.04.2012 2
  • 3. TU Graz - Knowledge Management Institute Aim  Explore bi-directional longitudinal influence patterns between social and content properties ? ?  Sample research questions  Does growth of a user's followers increase the number of authored tweets?  Does an increase of used URLs of users also increases their usage of hashtags?  … Philipp Singer 17.04.2012 3
  • 4. TU Graz - Knowledge Management Institute ? ? ? ? ? ? ? Philipp Singer 17.04.2012 4
  • 5. TU Graz - Knowledge Management Institute Experimental Setup • Twitter Dataset  1,500 random users chosen from public timeline  30 days time period (15.03.2011 – 14.04.2011)  Measure users’ social and content-related activities daily  Use social and content properties to describe and abstract those activities  Approach  Monitor and analyze how users‘ social activities and content-related activities (and related outcomes) co-evole over time (Wang and Groth 2010) Philipp Singer 17.04.2012 5
  • 6. TU Graz - Knowledge Management Institute Methodology  Multilevel autoregressive modeling  Predict future outputs based on past outputs  Variables are measured at different levels  Coefficients can vary from user to user  Overall coefficients determine influence between properties over time Philipp Singer 17.04.2012 6
  • 7. TU Graz - Knowledge Management Institute Does growth of a user's followers increase the number of authored tweets? ? Philipp Singer 17.04.2012 7
  • 8. TU Graz - Knowledge Management Institute Does an increase of used URLs of users also increases their usage of hashtags? ? Philipp Singer 17.04.2012 8
  • 9. TU Graz - Knowledge Management Institute ? ? ? ? ? ? ? Philipp Singer 17.04.2012 9
  • 10. TU Graz - Knowledge Management Institute Conclusions  Driven by social factors  Attention of other users motivates individuals  Social media hosts can adopt and use these techniques Philipp Singer 17.04.2012 10
  • 11. TU Graz - Knowledge Management Institute Limitations & Next Steps  One small random Twitter dataset   Apply techniques to further, larger datasets  Limited to some chosen properties   Produce different networks  Hashtag co-occurrence  Similarity networks  Try different model approaches  Analyze interface changes (e.g., recommender) Philipp Singer 17.04.2012 11
  • 12. TU Graz - Knowledge Management Institute Social properties influence content properties but not vice versa! Thanks for your attention! @ph_singer @clauwa @mstrohm philipp.singer@tugraz.at Philipp Singer 17.04.2012 12

Editor's Notes

  1. A record of phenomenon irregularly varying with time is called time series Predict future outputs based on past outputs