Group Membership and Diffusion in Virtual Worlds

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A study of how virtual goods spread in online communities and the unique impact of joining groups on that process. …

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.

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  • 1. Group Membership and Diffusionin Virtual Worlds David Huffaker School of Information, University of Michigan (Now at Google) In collaboration with Lada Adamic, Chun-Yuen Teng, Matthew Simmons andLiuling 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 amongadopters 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