A study of how virtual goods spread in online communities and the unique impact of joining groups on that process. Virtual goods continue to emerge in online communities, offering scholars an opportunity to understand how social networks can facilitate the diffusion of innovations. We examine the social ties for over one million user-to-user virtual goods transfers in Second Life, a popular 3D virtual world, and the unique role that groups play in the diffusion of virtual goods. The results show that individuals – especially early adopters – are more likely to adopt a virtual good when they belong to the same groups as previous adopters. We also find that groups exhibit bursty adoption, in which many individuals adopt in short succession. In addition, we show that adoption activity within a group depends on the group’s size and interactivity. Our work provides insights into theories of social influence and homophily.

1. Group Membership and Diffusion
in Virtual Worlds
David Huffaker
School of Information, University of Michigan
(Now at Google)
In collaboration with Lada Adamic, Chun-Yuen Teng, Matthew Simmons and
Liuling Gong

2. Virtual goods represent an important way to
measure information diffusion in social networks.
 Online communities and social media
provide a lens for understanding offline
social behavior.
 Virtual goods are exploding,
but we know little about
patterns of diffusion.
 Previous research has demonstrated
the importance of social ties in the
spread of information, but we’ve never
examined this at a large scale.

3. Diffusion has often been explained in terms of
social influence and homophily.
 Theories of Social Influence suggest that Social Influence
exposure to influential individuals increase the
likelihood of adopting similar beliefs [Monge
Contractor, 2003].
– Examples in online settings: Wu et. al, 2006; Bakshy et. al,
2009, Centola, 2010.
 Theories of Homophily suggest that individuals
seek out others with same self-categorization or
belong to same formal or informal groups [McPherson Homophily
2001].
– Examples in online settings: Aral et. al, 2009; Leskovec et.
al, 2007)
 Some argue there is really a complex
interplay of the two [Jackson, 2009; Shalizi
Thomas, 2011; Crandall et. al, 2008]

4. This research examines the role of social influence
and homophily in large-scale virtual goods adoption.
 What social factors increase the likelihood that a user
will adopt a virtual good?
 What role does group membership play in the adoption
of virtual goods?
 To what extent do
group characteristics
impact adoption?
We argue that both
individual and group factors play a
part in virtual goods diffusion.

5. Second Life is a free, 3D virtual world
with over one million users1.
[1]: Source: http://en.wikipedia.org/wiki/Second_Life

6. Users generate 95% of the content:
…from Casino Games to Teddy Bears…

7. Users can join up to 25 groups in Second Life.
 Most groups involving top virtual goods
sellers fall into the following categories:
[Huffaker et. al, 2010]
– Retail and Scripting (e.g., avatars, fashion,
furniture, etc.)
– Music / Clubs / DJ (e.g., fan clubs, venues,
and promotion)
– Lifestyle (e.g., interest, social or adult-
oriented groups)
– Land Rentals (e.g., landlords, vacation
rentals)
– Games (e.g., casinos, games of chance)

8. We rely on a large-scale data set of user-level and
group-level behavior.
 All virtual goods adoptions that took place in
Nov-Dec 2008 (N = 1,092,094; 546,047 unique
goods)
– Distance to first adopter; number of friends shared
with previous adopter
 Behavioral data for the unique users involved in
the adoptions (N = 235,467)
– Tenure in Second Life; Number of previous
adoptions, Active days; Number of groups;
 Group-level data for groups with 10+ members
and 5+ adopters (N = 61,722)
– Group size; Turnover; Social network measures
 Sampling strategy: Take one adopter and one Example of two types of virtual goods. While few
non-adopter from an adopting friend
assets enjoy widespread popularity, most were
– A second data set with a non-adopting friend
adopted by just a few individuals.

9. We focus on group similarity and ‘crowding factor’
to understand the impact of groups.
 Crowding Factor. Percentage of
adopters in each of the user’s groups.
 Group Similarity. Cosine similarity
between the group membership of an
adopter and the groups of previous
adopters.
Example of Crowding factor. As
more members become adopters,
other might be more likely to follow
suit.
Example of Group Similarity. When users share a lot of overlapping groups with
previous adopters, they have more potential contact with a new virtual good.

10. Group similarity and crowding factor have a strong
impact on the likelihood of adoption.
 We use a logistic regression model to Estimate
CV
predict the likelihood of adopting an
asset after a friend adopts it [Bakshy et. al, # Days in Second Life
–.01
.50
2009].
# Adopting Friends
–.44
.53
 We use cross-validation (10-fold) in # of Groups
–.52
.53
order to estimate how well the
Distance from 1st Adopter
–.07
.56
predictive model performs. The left-most
column shows the individual CVs. # Other Adopted Assets
.48
.58
 Group similarity and crowding # Friends with Previous .34
.58
factor are the most predictive Adopters
individuals variables in Crowding Factor
.10
.61
determined if an adopter’s
friends will adopt. Group Similarity
.23
.63
Combined Model
.68
 Note: We applied the same analysis to a data set
where the adopter and non-adopter do not share a
All variables are significant, p .001
common adopting friend and find consistent results.

11. The number of adopting friends has a flatter slope.
Probability of adoption based on
group similarity, crowding factor and
number of adopting friends among
adopters and non-adopters who have
a friend who previously adopted the
virtual good. All predictors were
standardized.

12. Early and late adoption shows contrast in their
predictability when considering group membership.
 We separated adoptions into early (first Popular
Less Least
20% of time span), middle and late adoption Popular
Popular
(last 25% of time span).
Previous adopting friend
 We classify virtual goods into popular (100+
adopters), less popular (between 6 and 12 Early
.76
.74
.79
adopters) and least popular (6 adopters)
Middle
.68
.71
.73
 Group membership variables are Late
.67
.67
.68
more predictive for early adopters
No previous adopting friend
with an adopting friend and late
adopters with no adopting friend.
Early
.82
.83
.84
Middle
.85
.86
.87
Late
.90
.88
.90

13. Bursts of adoption tend to occur within groups.
Figure. The number of groups
required to cover all adopters
of a particular virtual good,
provided they are members of
at least one group.
The number of groups required
to cover the adopters of an
virtual good is smaller than the
number that would be required
to cover a randomly assembled
group of the same size.
 Asset adoption time series can reveal ‘bursty behavior’ where an
unexpectedly large number of adoptions occur within some time window.
– We identify virtual goods where 90% of adoptions occur in one week, along with those where 20%
of adoptions occur in one week.
 Adopters of bursty assets are more likely to share groups and to be friends.

14. Groups with strong signatures of interaction are
more responsible for the spread of virtual goods.
 A high degree of clustering and
strongly connected components
are positively correlated with
both within-group transfers and
total adoptions by group
members.
 Groups with more highly connected
individuals (i.e., average degree) are
positively correlated with transfers but
negatively correlated with total
adoptions.
– Interconnectedness creates boundaries?
Taste-making. Less innovation can enter.
 Group size shows a negative correlation Network visualizations of transaction ties (in black) and
for transfers and adoptions, but opposite social ties (in gray) for the lowest (Top) and highest
is true for groups with highly active (Bottom) frequency of adoption for a random sample of
members.
groups with 20 members.
– Problem with large groups?

15. Groups (and group membership) play an important
role in the diffusion process.
 When an individual belongs to many of the same groups as other adopters—
and when an individual’s groups are populated by adopters—she is more
likely to adopt a virtual good.
 There are, of course, limitations:
– Confounding nature of social influence and homophily We need to disentangle how group
membership represents shared interest vs. actually promoting a virtual good.
– Generalizability Second Life to other online communities. We recognize the unique
context and community of SL.
 Further support that individual influence is not the sole factor in the diffusion
process; considering larger collectives can better explain the likelihood of adopting
an innovation.

16. Acknowledgements
NetSI Lab
Lada Adamic
http://www.davehuffaker.com