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.
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.