Social Networks and Social Simulation of 3D Online Communities

Stereotypical views of games

Games as social activities

Games as communities

Social Networks and Social Simulation of 3D Online Communities (Jim) CS Ang Research Fellow Centre for HCI Design

Structure of presentation

Brief introduction to sociability

Analysing social networks

Study 1: Social network modelling

Simulating social networks

Study 2: Simulation modelling

Conclusion

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

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

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”

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

Study 1: Social Network Modelling Understanding the network characteristics of social interaction

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

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

P* model (Robins et al., 2007)

Results

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?

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)

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

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

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

What about social simulations?

Can simulations be useful in simulating social activities

What about simulating social network (of online communities?)

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!

Study 2: Simulation Model

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

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

The simulation with Netlogo

Qualitative validation Help interaction Chat interaction

Quantitative validation

Social budget

Social budget and in degree centrality social budget = 0 social budget = 3

Social budget and out degree centrality social budget = 0 social budget = 3

Activeness factor

Cohesiveness factor

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