This document summarizes a research paper about factors that predict whether users will unfollow other users on Twitter. The researchers analyzed structural properties of 911 Twitter users' social networks, such as number of followers, reciprocity of follows, and common connections. They found that reciprocity, network density, prestige ratio, follow-back rate, and number of common neighbors strongly predicted whether ties would be broken through unfollowing. The paper provides more detailed analysis using multi-level logistic regression to model how network structure impacts breaking ties on Twitter.

1. 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.

2. blue = unfollows

3. 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

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

5. theory background
tie strength
embeddedness within networks
power & status

6. 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

7. 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

8. effect of number of followers (none):
how many
followers a
user has,
versus the
tendency of
people to
stop following
that user.

9. 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 e!ects
(because we looked at followers of 715 nodes). See paper for a
more robust analysis showing the (large) e!ect of reciprocity.

10. effect of reciprocity (another view):
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!) .

11. 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 e!ect suggests that “follow-back ratio”
is a indeed a good measure of status on Twitter.

12. 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 e!ects
(because we looked at followers of 715 nodes). See paper for a
more robust analysis showing the e!ect of common neighbors.

13. 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

14. some results
… explaining tie-breaks
effect of tie strength on breaking of ties.
*** dyadic reciprocity
*** network density
*** highly statistically significant

15. 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

16. some results
… explaining tie-breaks
effect of embeddedness on breaking of ties.
*** common neighbors
*** highly statistically significant

17. limitations & future work
only two snapshots: add more
additional (non-structural) variables
(frequency of posting?)

18. 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.

19. thank you
mornaaman.com
@informor
Rutgers SC&I
Social Media Information Lab