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Unfollowing on twitter
 

Unfollowing on twitter

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    Unfollowing on twitter Unfollowing on twitter Presentation Transcript

    • unfollowing on twitter
      FundaKivran-Swaine, PriyaGovindan, Mor NaamanRutgers University | School Media Information Lab
      Article: The Impact of Network Structure on Breaking Ties in Online Social Networks: Unfollowing on Twitter. To Appear, CHI 2011.
    • blue = unfollows
    • big story
      online social networks – large proportion of activity on the Web.
      generate a better understanding of the dynamics
      validate theories from social sciences in these new and important settings
    • what structural social network properties of nodes and dyads predict the breaking of ties?
    • theory background
      tie strength
      embeddedness within networks
      power & status
    • data
      content data set (911 users) from Naaman , Boase, Lai (2010)social network data from Kwak et al. (2010)
      715 seed nodes
      245,586 “following” connections to seed nodes
      30.6 % dropped between 07/2009 & 04/2010
    • analysis
      * independent variables (computed)
      seed properties follower-count, follower-to-followee ratio, network density, reciprocity rate, follow-back rate
      follower propertiesfollower-count, follower-to-followee ratio
      dyad propertiesreciprocity, common neighbors, common followers, common friends, right transitivity, left transitivity, mutual transitivity, prestige ratio
    • effect of number of followers (none):
      how many followers a user has, versus the tendency of people to stop following that user
    • effect of reciprocity (large):
      how many “follow” relationships were terminated, when connection was reciprocated, and when it wasn’t.
      note: this analysis is not robust due to between-node effects (because we looked at followers of 715 nodes). See paper for a more robust analysis showing the (large) effect of reciprocity.
    • effect of reciprocity (another view):
      The user’s tendency to reciprocate, versus the tendency of users to stop following them
      note: this effect is mostly explained by the individual relationships (previous slide), but included here for dramatic purposes (steep curve!) .
    • effect of follow-back rate:
      what percentage of people the user follow that follow them back, versus the tendency of people to stop following the user
      note: this strong effectsuggests that “follow-back ratio” is a indeed a good measure of status.
    • effect of common neighbors:
      the proportion of unfollows amongst pairs with 0,1,2,…,15 common neighbors
      note: again, this analysis is not robust due to between-node effects (because we looked at followers of 715 nodes). See paper for a more robust analysis showing the effect of common neighbors.
    • in-depth analysis
      * the details you did not want to know…
      * multi-level logistic regression (dyads/edges nested within seed nodes)
      * three models; full one includes seed, follower, and dyadic/edge variables
      * full details in the paper
    • some results
      … explaining tie-breaks
      effect of tie strength on breaking of ties.
      *** dyadic reciprocity
      *** network density
      *** highly statistically significant
    • some results
      … explaining tie-breaks
      effect of power & status on breaking of ties.
      *** prestige ratio
      *** follow-back rate
      *** f’s follower-to followee ratio
      *** dyadic reciprocity
      *** highly statistically significant
    • some results
      … explaining tie-breaks
      effect of embeddedness on breaking of ties.
      *** common neighbors
    • limitations & future work
      only two snapshots: add more
      additional (non-structural) variables (frequency of posting?)
    • for more details
      http://www.ayman-naaman.net/?p=667
      FundaKivran-Swaine, PriyaGovindan and Mor Naaman (2011). The Impact of Network Structure on Breaking Ties in Online Social Networks: Unfollowing on Twitter. In Proceedings, CHI 2011.
    • thank you
      mornaaman.com
      @informor