Social Networks and Social Simulation of 3D Online Communities
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Transcript of 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
en
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.