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The Effect of User Features on Churn in Social Networks
Jeffrey Chan, Conor Hayes, Marcel Karnstedt
Digital Enterprise Research Institute (DERI)
NUI Galway, Ireland
Email: firstname.lastname@deri.org
Social sites and services rely on the continuing activity,
good will and behaviour of the contributors to remain
viable. There has been little empirical study of the mechanisms
by which social sites maintain a viable user base.
Such studies would provide a scientific understanding of
the patterns that lead to user churn and the community
dynamics that are associated with reduction of community
members – primary threats to the sustainability of
any service. By churn we refer to the loss of users, as
one indicator for decreasing community value, implicitly
encoding the idea that a user no longer finds a service
useful or valuable and has moved elsewhere. In this paper
we explore the relation between a user’s value within a
community - constituted from various user features - and
the probability of a user churning.
In studies of churn behaviour of customers of telcom
networks, a user’s probability of churning has been linked
to the churning behaviour of neighbours in his/her social
network. This has recently also been observed in online social
networks [1]. These effects are illustrated in Figure 1,
showing an individual’s probability to churn in relation
to the number k of his neighbours that already churned.
In absence of explicit friendship links in forum data, an
intuitive network of influence can be based on reply-to
relations between users. Each of the reply graphs represents
different definitions of who is considered as a neighbour.
probability to churn
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
out ≥ 2 posts
bid. ≥ 2 posts
out ≥ 5 posts
bid. ≥ 5 posts
0
0 5 10 15 20 25
number k of churning neighbours
Figure 1. Network effects on the probability to churn
in an online discussion forum. Four different reply
graphs represents different definitions of influence.
The labels with out refer to influence being based
on uni-directional communications, while bid. refers
to influence being based on bidirectional communications.
The numbers (>= 2 and >= 5) represent two
levels of influence based on volume of communications.
87
Harith Alani, Matthew Rowe
Knowledge Media Institute (KMI)
The Open University, Milton Keynes, UK
Email: {h.alani, m.c.rowe}@open.ac.uk
In this paper, we examine relationships between user
value and churn. Definitions of user value [2] refer to a
collection of user features. The following are examples of
user features that we have identified from the literature that
contribute to user value:
• Structural and Social Network Features: in-degree and
out-degree exponents, centrality and betweeness
• Reciprocity Features: average reply time to posts
• Persistence/productivity Features: average post per
thread and frequency of posting
• Popularity Features: number of in-neighbours, the
percentage of replies to posts
• Sentiment Features: average polarity of posts
Building on our previous work [1], we explore the correlation
between the above features and churn probability
and influence, identifying key indicators of churn within
a community. For our experiments over a year’s worth of
data, we profile contributors in an online bulletin board
by extracting the above salient behavioural and structural
features, which are used to describe user value. Our
approach employs time-series analysis, identifying links
between certain user value features and their evolution
with time, and the probability of an individual leaving
the community. Our hypothesis is that users which display
different behavioural, content and structural characteristics
in the underlying social network will tend to have different
influence on churn. By this we identify features of contributors
that are implicitly recognised by other users as
contributing to the value of the community. This provides
an important contribution to the analysis of the relationship
between user value, user churn and community value in
general. It produces an understanding of the behavioural
patterns associated with the loss of community members,
eventually enabling community hosts to identify, early-on,
that users may leave the community.
References
[1] M. Karnstedt, T. Hennessy, J. Chan, P. Basuchowdhuri, C. Hayes,
and T. Strufe, “Churn in social networks,” in Handbook of Social
Network Technologies and Applications, B. Furht, Ed. Springer
Verlag, 2010, pp. 185–220.
[2] J. J. Phillips, “Human capital measurement: A challenge for the clo,”
Clo Media, Tech. Rep., 2003.