Unfollowing on twitter
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Transcript of Unfollowing on twitter
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unfollowing on twitter Funda Kivran-Swaine, Priya Govindan, Mor Naaman
Rutgers 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.
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blue = unfollows
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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
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what structural properties of the social network of nodes and dyads predict the breaking of ties (unfollows) on Twitter?
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theory background
tie strength embeddedness within networks power & status
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data
user 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
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analysis * independent variables (computed)
seed properties follower-count, follower-to-followee ratio, network
density, reciprocity rate, follow-back rate
follower properties follower-count, follower-to-followee ratio
dyad properties reciprocity, common neighbors, common followers, common friends, right transitivity, left transitivity, mutual transitivity, prestige ratio
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e!ect of number of followers (none):
how many followers a user has, versus the tendency of people to stop following that user.
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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 e!ects (because we looked at followers of 715 nodes). See paper for a more robust analysis showing the (large) e!ect of reciprocity.
e!ect of reciprocity (large):
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The user’s tendency to reciprocate, versus the tendency of users to stop following them
note: this e!ect is mostly explained by the individual relationships (previous slide), but included here for dramatic purposes (steep curve!) .
e!ect of reciprocity (another view):
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e!ect 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 e!ect suggests that “follow-back ratio” is a indeed a good measure of status on Twitter.
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the proportion of unfollows amongst pairs with 0,1,2,…,15 common neighbors
note: again, this analysis is not robust due to between-node e!ects (because we looked at followers of 715 nodes). See paper for a more robust analysis showing the e!ect of common neighbors.
e!ect of common neighbors:
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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
* complete details: in the paper
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some results
… explaining tie-breaks
e!ect of tie strength on breaking of ties.
*** dyadic reciprocity *** network density
*** highly statistically significant
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some results
… explaining tie-breaks
e!ect of power & status on breaking of ties.
*** prestige ratio *** follow-back rate *** f’s follower-to followee ratio *** dyadic reciprocity
*** highly statistically significant
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some results
… explaining tie-breaks
e!ect of embeddedness on breaking of ties.
*** common neighbors
*** highly statistically significant
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limitations & future work
only two snapshots: add more
additional (non-structural) variables (frequency of posting?)
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for more details
http://www.ayman-naaman.net/?p=667
Funda Kivran-Swaine, Priya Govindan and Mor Naaman (2011). The Impact of Network Structure on Breaking Ties in Online Social Networks: Unfollowing on Twitter. In Proceedings, CHI 2011.
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Rutgers SC&I
Social Media Information Lab
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