social networks and social simulation of 3d online communities

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Stereotypical views of games

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Games as social activities

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Games as communities

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Social Networks and Social Simulation of 3D Online Communities

(Jim) CS Ang

Research Fellow

Centre for HCI Design

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Structure of presentation

• Brief introduction to sociability• Analysing social networks

– Study 1: Social network modelling

• Simulating social networks– Study 2: Simulation modelling

• Conclusion

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3D virtual worlds as communities

• 3D is not only an additional graphical dimension

• Beyond chatting• The whole range of human (even non-

human?) activities– Flying – Monster slaying– Dungeon exploration

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Sociability studies of 3D virtual worlds

• These studies have treated individuals as the unit of analysis

• E.g. looked at “the amount of time spent by individual players and the relation to game character levels”; “types of message individuals post”

• It is about what the individuals are and what the individuals do

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What about relations?

• Social Network Analysis• The relationships of individuals as well as the patterns

and implications of these relationships have on the individuals

• E.g. we can look at “whether the player is likely to gain higher level if she interacts with certain groups of players”

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Why bother studying them?

• Understanding user online interaction: shopping behaviour, learning, socialising, play, etc

• Utilising social networks to support these behaviour• Designing social technological systems that support

social networks

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Study 1: Social Network Modelling

Understanding the network characteristics of social interaction

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The WoW guild community

• Online communities function as a major mechanism of socialisation in WoW

• Guilds give the players a chance to run a virtual association which has formalised membership and rank assignments that encourage participation

• Each guild usually has a leader and several guilds could team up in a battle

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Methods

• 1944 lines of guild messages were collected in 30 hours of observation

• Messages were categorised into seven interaction types: “give help”, “ask for help”, “group management” “coordination” “friendly remark”, “game chat” and “real life chat”

• Socio-matrices (who-talk-to-whom matrices) was constructed

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P* model (Robins et al., 2007)

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Results

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Ask for help and give help

• “ask for help” interaction has positive tendency of in-K-star pattern (0.5231)

• “give help” interaction has positive tendency of out-K-star pattern (1.0267 )

• Finding 1: guild players did tend to ask for help from a specific group of players

• Why?

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Friendly, game chat and real life chat

• The reciprocity parameter shows that friendly remark (1.2829) and game chat (3.0757) networks have significantly higher reciprocity than random networks

• Finding 2: chatting interaction was inclined to be reciprocated

• Friendly interaction has a significant in-K-star parameter (0.5297)

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Friendly, game chat and real life chat

Player_R: […] where in [deadmine] I can find the items needed [for] the Oh Brother [quest]

Player_S: they're in the undead part

Player_R: thanks a lot :)

• Finding 3: friendly remark interaction tends to result in a high power distance network

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What-if…?

• P*model gives us a statistical description of the social network of an existing community

• In many cases, we might want to know how policy intervention/occurrence of unexpected events will transform the social network of the community

• There is a need to explore what-if situations• We can explore different design alternatives• Through simulations

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What are simulations?

• Computational models that mimic the target system• To understand the behaviour of the system• To explore what-if hypothetical situations• Generation and analysis of data• The contexts of use: safety engineering, training,

education, military, biology, ecosystem

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What about social simulations?

• Can simulations be useful in simulating social activities• What about simulating social network (of online

communities?)

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Agent-based simulation

• AI like agents with goal, they will act, react and interact with others and with the environment

• Agents can be programmed with simple rules but the behaviour of the system as a whole can be complex

• It is non-linear and cannot be predict statistically, just like many real social events

• Results are emergent!

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Study 2: Simulation Model

Can we “grow” the observed social network from bottom up?

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Rule formalisation

• Based on the empirical observation of existing social networks

• Focused on three interactions: ask help, give help, chat• Qualitative and quantitative results are formalised into

programming language

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The simulation with Netlogo

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Qualitative validation

Help interaction Chat interaction

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Quantitative validation

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Social budget

0

0.05

0.1

0.15

0.2

0.25

0.3

0 1 2 3 4 5 6 7 8 9 10

social budget

deg

ree c

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trali

sati

on

out degree in degree

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Social budget and in degree centrality

social budget = 0 social budget = 3

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Social budget and out degree centrality

social budget = 0 social budget = 3

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Activeness factor

0

0.02

0.04

0.06

0.08

0.1

0.12

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

activeness factor

density transitivity

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Activeness factor

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

activeness factor

deg

ree c

en

trali

sati

on

out degree in degree

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Cohesiveness factor

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

cohesiveness factor

density transitivity

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Cohesiveness factor

reciprocity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

cohesiveness factor

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Conclusion

• With p* modelling study, we can only understand the characteristic of the existing community

• With simulation, we can understand the casual effect of different factors to network characteristics

• we could infer how design can affect the growth of the community

• E.g. a reward system that will increase the activeness factor of individuals drastically can result in more activities but a risk of unbalanced growth

• System that encourage neighbour interaction will increase reciprocity, but will reduce activities

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Potentials in HCI/CMC research

• Can answer fundamental research questions• Help practitioners design and regulate online

communities• Incorporated into existing HCI methods• Observational/experimental studies at individual/micro

level• to understand the community/macro level

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Working papers

• Social Roles and Positions of Guild Players in Massively Multiplayer Online Games: a Social Network Analytic Perspective.

• Interaction Networks and Patterns of Guild Community in Massively Multiplayer Online Games.

• Social Interaction Networks Simulation in Virtual Communities.