Unfollowing on twitter
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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