Modelling and Analysis of User Behaviour in Online Communities

Modelling and Analysis of User Behaviour in Online

Communities Sofia Angeletou, Matthew Rowe and Harith Alani

Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom

International Semantic Web Conference 2011. Bonn, Germany. 2011

Modelling and Analysis of User Behaviour in Online Communities

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The Utility of Online Communities

• Online communities yield value in terms of:– Idea generation– Customer support– Problem solving

• Managing and hosting communities can be– Expensive– Time-consuming

• Large investments in communities, therefore they must:– flourish and remain active– remain… ‘healthy’


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Increasingly Active Community

What did the community look like at the point?


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Increasingly Inactive Community

What were the conditions at this point?


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Gauging Health

• How can we gauge community health?– Post Count?– User Count?– Communication/Interaction?– Behaviour?

• Domination of one behaviour could lead to churn– Preece, 2000

• Behaviour in online community is influenced by the roles that users assume– Preece, 2001

• To provide health insights we need to monitor behaviour over time– Combined with basic health metrics (e.g. post count)


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Supporting Community Owners

1. Monitor and capture member activities

2. Analyse emerging behaviour over time

3. Understand the correlation of behaviour with community evolution

4. Learn when to intervene to influence the community


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Supporting Community Owners

1. Monitor and capture member activities

2. Analyse emerging behaviour over time

3. Understand the correlation of behaviour with community evolution

4. Learn when to intervene to influence the community


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Contributions

• Ontology to model behavioural roles and behaviour features– Capturing time stamped user attributes

• Method to infer user roles in online communities– Using semantic rules

• Analysis of community health through role composition– Identifying composition patterns for healthy communities


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Outline

• Behaviour Ontology• Behaviour Features• Community Roles• Approach for Behaviour Analysis

– Constructing Semantic Rules– Applying Semantic Rules

• Analysis of Community Health• Predicting Community Health• Findings• Future Work• Conclusions


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Behaviour Ontology

http://purl.org/net/oubo/0.3

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Behaviour Features

• In-degree Ratio– Proportion of users that reply to user

• Posts Replied Ratio– Proportion of posts by that yield a reply

• Thread Initiation Ratio– Proportion of threads started by

• Bi-directional Threads Ratio– Proportion of threads where is involved in a reciprocal action

• Bi-directional Neighbours Ratio– Proportion of ‘s neighbours with whom a reciprocal action has

taken place• Average Posts per Thread

– Mean number of posts in the threads that has participated in• Standard Deviation of Posts per Thread

– Standard deviation of posts in the threads that has posted in



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Community Roles

Elitist

Grunt

Joining Conversationalist

Popular Initiator

Popular Participant

Supporter

Taciturn

Ignored

Jeffrey Chan, Conor Hayes, and Elizabeth Daly. Decomposing discussion forums using common user roles. In Proc. Web Science Conf. (WebSci10), Raleigh, NC: US, 2010.


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Community Roles

Elitist

Grunt


Popular Initiator

Popular Participant

Supporter

Taciturn

Ignored



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Community Roles

Elitist

Grunt


Popular Initiator

Popular Participant

Supporter

Taciturn

Ignored



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Community Roles

Elitist

Grunt


Popular Initiator

Popular Participant

Supporter

Taciturn

Ignored



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Community Roles

Elitist

Grunt


Popular Initiator

Popular Participant

Supporter

Taciturn

Ignored



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Community Roles

Elitist

Grunt


Popular Initiator

Popular Participant

Supporter

Taciturn

Ignored



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Community Roles


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Constructing Rules

Structural, social network, reciprocity, persistence, participation

Feature levels change with the dynamics of the community

Based on related work, we associate roles with a collection of feature-to-level mappingse.g. in-degree -> high, out-degree -> high

Run rules over each user’s features and derive the community role composition


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Applying Rules

CONSTRUCT { ?role a ?t . ?this social-reality:count_as ?role . ?context a social-reality:C . ?role social-reality:context ?context . ?temp a oubo:TemporalContext . ?forum a sioc:Forum . ?forum oubo:belongsToContext ?context . ?temp oubo:belongsToContext ?context} WHERE { BIND (oubo:fn_getRoleType(?this) AS ?type) . BIND(smf:buildURI("oubo:Role{?type}") AS ?t) . .....}


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Applying Rules


1. SPIN function fn_getRoleType() matches the user (?this) with the relevant role type

http://spinrdf.org/spin.html


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Applying Rules


2. Build the URI for the behaviour role class of the user, based on the ?type match


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Applying Rules


3. The user (?this) is associated with the role in the given time span (?temp) and forum (?forum)


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Analysis of Community Health

• How is community role composition associated with activity?

• Dataset– Irish community message board: Boards.ie– All posts used from 2004 – 2006– Selected 3 forums for analysis

• F246: Commuting and Transport• F388: Rugby• F411: Mobile Phones and PDAs

• Measured at 12-week increments:– Forum composition (% of roles)

• E.g. 20% elitists, 10% grunts, etc– Number of posts


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Analysis: Results (1)

Forum 246 – Commuting and Transport


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Forum 388 – Rugby Forum 411 – Mobile Phones and PDAs


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Forum 388 – Rugby Forum 411 – Mobile Phones and PDAs


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Predicting Community Health

• Can we predict community health from role composition?

1. Predict either an increase or decrease in activity– Features: roles and percentages– Class label: increase/decrease– Performed 10-fold cross validation with J48 decision tree

2. Predict post count from role composition– Independent variables: roles and percentages– Dependent variable: post count– Induced linear regression model and assessed the model


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Prediction: Results (1)


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Prediction: Results (2)


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Findings

1. Active communities contain more Elitists and Popular Participants

2. Unhealthy community contain more Tactiturns and Ignored users

3. Communities exhibit idiosyncratic compositions

4. A stable, mixed composition increases activity

=

=


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Future Work

• Micro-level role analysis– Development of a ‘role lifecycle’

• Identification of key community users– To avoid such users ‘churning’

• Explore alternative methods for role labelling– Current approach misses ~29% of users

• Extend analysis to other community types– Enterprise communities– Social networking platforms


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Conclusions

• Presented an approach to label users with roles based on their behaviour– Ontology captures user behaviour as numeric attributes– Semantic rules are employed to infer user roles

• Behaviour roles are only a subset of the literature– Roles differ based on the community type– Our approach is portable to other roles

• Correlated community composition with activity– Increase in Elitists and Popular Participants = increased

activity– Increase in Taciturns and Ignored = decreased activity– Stable, mixed composition = increased health


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Questions?

Web: http://people.kmi.open.ac.uk/roweEmail: [email protected]: @mattroweshow

Modelling and Analysis of User Behaviour in Online Communities

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