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Understanding co-evolution of social and content networks on Twitter

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Workshop presentation at #msm2012 and #www2012 conference about "Understanding co-evolution of social and content networks on Twitter".

Workshop presentation at #msm2012 and #www2012 conference about "Understanding co-evolution of social and content networks on Twitter".

Published in: Technology, Business

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