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

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’

Modelling and Analysis of User Behaviour in Online 1
Communities

Increasingly Active
Community

What did the community look like at the point?

Communities

Increasingly Inactive
Community

What were the conditions
at this point?

Communities

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)

Communities

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

Communities

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 ui
• Posts Replied Ratio
– Proportion of posts by ui that yield a reply
• Thread Initiation Ratio
– Proportion of threads started by ui
• Bi-directional Threads Ratio
– Proportion of threads where ui is involved in a reciprocal action
• Bi-directional Neighbours Ratio
– Proportion of ui‘s neighbours with whom a reciprocal action has
taken place
• Average Posts per Thread
– Mean number of posts in the threads that ui has participated in
• Standard Deviation of Posts per Thread
– Standard deviation of posts in the threads that ui 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.

Communities

Community Roles

Elitist
Grunt
Popular Initiator
Popular Participant
Supporter
Taciturn
Ignored

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Community Roles
T abl e 1. Roles and t he feat ure-t o-level mappings

R ol e Feat ur e L evel
E l i t i st I n-D egr ee R at i o l ow
B i -di r ect i onal T hr eads R at i o hi gh
B i -di r ect i onal N ei ghb our s R at i o l ow
G r unt B i -di r ect i onal T hr eads R at i o m ed
B i -di r ect i onal N ei ghb our s R at i o m ed
A ver age Post s p er T hr ead l ow
ST D of Post s p er T hr ead l ow
Joi ni ng Conver sat i onal i st T hr ead I ni t i at i on R at i o l ow
A ver age Post s p er T hr ead hi gh
ST D of Post s p er T hr ead hi gh
Popul ar I ni t i at or I n-D egr ee R at i o hi gh
T hr ead I ni t i at i on R at i o hi gh
Popul ar Par t i ci pant s I n-D egr ee R at i o hi gh
T hr ead I ni t i at i on R at i o l ow
A ver age Post s p er T hr ead m ed
ST D of Post s p er T hr ead m ed
Supp or t er I n-D egr ee R at i o m ed
B i -di r ect i onal T hr eads R at i o m ed
B i -di r ect i onal N ei ghb our s R at i o m ed
T aci t ur n B i -di r ect i onal T hr eads R at i o l ow
B i -di r ect i onal N ei ghb our s R at i o l ow
A ver age Post s p er T hr ead l ow
ST D of Post s p er T hr ead l ow
I gnor ed Post s R epl i ed R at i o l ow

Communities

Constructing Rules
Structural, social network, Feature levels change with the
reciprocity, persistence, participation dynamics of the community

Run rules over each user’s features Based on related work, we associate
and derive the community role composition roles with a collection of feature-to-level
mappings
e.g. in-degree -> high, out-degree -> high

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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
CONSTRUCT {
?role a ?t .
} WHERE {
.....
} 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
CONSTRUCT {
?role a ?t .
} WHERE {
.....
} 2. Build the URI for the behaviour role class of the
user, based on the ?type match

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Applying Rules
CONSTRUCT {
?role a ?t .
} WHERE {
.....
} 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 246 – Commuting Forum 388 – Rugby Forum 411 – Mobile Phones
and Transport and PDAs

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Forum 246 – Commuting and Transport

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Forum 246 – Commuting Forum 388 – Rugby Forum 411 – Mobile Phones
and Transport 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

Communities

having eit her increased (pos) or decreased (neg) since t he previous t ime window.
For our classificat ion t ask we used t he J48 decision t ree classifier in a 10-fold
cross validat ion set t ing (due t o t he Prediction: dat aset s) by: first, iden-
limit ed size of t he Results (1)
t ifying increases and decreases in each of t he forums, and secondly, ident ifying
act ivity changes across communit ies, by combining forum dat aset s t oget her int o
a single dat aset . To report on t he performance of our approach we used preci-
sion, recall, f-measure (set t ing β = 1) and t he area under t he Receiver Operat or
Charact erist ic Curve (ROC).
T ab l e 2. Result s from det ect ing changes in act ivity using community composit ion

For um P R F1 ROC
246 0.799 0.769 0.780 0.800
388 0.603 0.615 0.605 0.775
411 0.765 0.692 0.714 0.617
A ll 0.583 0.667 0.607 0.466

Table 2 present s t he result s from our classificat ion experiment s. For forum
246 we achieve t he highest F1 value due t o t he act ivity in t he forum st eadily
increasing over t ime and t he precision value indicat ing t hat in t his forum t he
composit ion pat t erns account for fluct uat ions in act ivity. For forum 388 we re-
turn t he lowest F1 value, indicat ing t hat t he variance in act ivity renders t he
predict ion of act ivit y increase difficult wit hin t his forum, t his could possibly
be due t o t he seasonal fluct uat ions in int erest surrounding t he rugby season.
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t his analysis we have ident iﬁed four key take-home messages:
1. Healt hy communit ies cont ain more elit ist s and popular part icipant s.
2. Unhealt hy communit ies cont ain Prediction: Results (2)
many t acit urns and ignored users.
3. Communit ies exhibit idiosyncrat ic composit ions, t hus reﬂect ing t he differing
dynamics t hat are required/ exhibit ed by individual communit ies.
4. A st able composit ion, wit h a mix of roles, increases community healt h.

T ab l e 3. Linear regression model induced from t he forum composit ion of f388

R ol e E st ’ Coeffici ent St andar d E r r or t -Val ue P ( x > t )
Joi ni ng Conver sat i onal i st 69.20 43.82 1.579 0.1751
Popul ar I ni t i at or s 173.41 54.72 3.169 0.0248 * *
T acit ur ns -135.97 101.91 -1.334 0.2397
Supp or t er s -266.53 109.60 -2.432 0.0592 *
E l i t i st s -105.19 55.88 -1.882 0.1185
Popul ar Par t i ci pant s 372.44 103.24 3.608 0.0154 * *
I gn or ed -75.69 33.39 -2.267 0.0727 *
2
Sum m ar y: R es. St E r r : 311.5, A dj R : 0.8514, F 7 , 5 : 10.82, p-val ue: 0.0092
Si gni f. codes: p-val ue < 0.001 * * * 0.01 * * 0.05 * 0.1 . 1

5 D iscussion and Fut ur e W or k
T he communit ies we chose t o analyse in t his paper were forums from Boards.ie.
It is possible of course t hat different behavioural pat t erns could emerge when
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analysing different communit ies. However, t here is no reason t o assume t hat our

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/rowe
Email: m.c.rowe@open.ac.uk
Twitter: @mattroweshow

Communities

Modelling and Analysis of User Behaviour in Online Communities

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