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.

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 properties of the social network of nodes and dyads predict the breaking of ties (unfollows) on Twitter?

theory background

tie strength embeddedness within networks power & status

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

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

e!ect of number of followers (none):

how many followers a user has, versus the tendency of people to stop following that user.

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):

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):

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.

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:

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

some results

… explaining tie-breaks

e!ect of tie strength on breaking of ties.

*** dyadic reciprocity *** network density

*** highly statistically significant

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

some results

… explaining tie-breaks

e!ect of embeddedness on breaking of ties.

*** common neighbors

*** highly statistically significant

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

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.

mornaaman.com

@informor

Rutgers SC&I

Social Media Information Lab

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