summary of current work. presented to the IFIP Working Group on Social Semantics

1. Behaviour and Health Analysis of
Online Communities
Harith Alani
Knowledge Media institute
twitter.com/halani
delicious.com/halani
linkedin.com/pub/harith-alani/9/739/534
facebook.com/harith.alani
IFIP WG 12.7 – Galway, October 12, 2012

2. Milton Keynes

3. Knowledge Media institute (KMi)
• Set up in 1995 to bring the OU to the forefront of
research and development
• Different from the rest of the OU
– 100% focus on research and development
• has around 60 researchers, lead by 8 senior staff
• Over 100 projects, and 1000 publications
• Core research areas:
– Future Internet, Knowledge Management, Multimedia &
Information Systems, Narrative Hypermedia, New Media
Systems, Semantic Web & Knowledge Services, Social Software

4. 0
0.2
0.4
0.6
0.8
1
1.2
1 5 9 13 17 21 25 29 33 37 41 45
H-Index F2F Degree F2F Strength
healthy scien fic & social
profiles. freq chairs/OCs
in LSS team
good scien fic, and
social signals
shy scien st?
outsider,
high profile
Students, PG, developers.
who's the next star researcher?
First encounter with ‘Behaviour analysis’
• Integration of physical
presence and online
information
• Semantic user profile
generation
• Logging of face-to-face contact
• Social network browsing
• Analysis of online vs offline
social networks

5. eParticipation is about reconnecting ordinary people with politics and
policy-making [….] Governments and the EU institutions working with citizens
to identify and test ways of giving them more of a stake in the policy-shaping
process, such as through public consultations on new legislation
• Problem is that people don’t use government portals, minister blogs, opinion collecting web sites
• Instead, they use social media
• Targeted at developing methods to understand and manage the business, social and economic
objectives of the users, providers and hosts and to meet the challenges of scale and growth in
large communities
• Management and risk analysis in business online communities
• Scalable, real time analysis of behaviour, value, and health of communities
http://robust-project.eu/
http://wegov-project.eu/

6. “specifically designed for
politicians, enabling them to monitor debate,
filter out the background "noise" and zoom in
on what people are saying about them and
their policies in a particular geographical area”
http://www.wegov-project.eu/

7. Management of Online Communities Health
– Which are strong and healthy?
– Which are aging and withering?
– What health signs should we look
for?
– How these signs differ between
different communities?
• Evolution
– Can we predict their future
evolution?
– How can their evolution be
influenced?
• Behaviour
– How can behaviour be detected?
– How are their member behaving?
– Which behaviour is good/bad in
which community type?
– What’s the lifecycle of behaviour
roles?
• Goals and Values
– What are the goals of these
communities?
– Are they fulfilling the goals of
their owners?
– Are they fulfilling the goals of
their members?
– Which members are valuable?

8. 8
Tools for monitoring social networks

9. http://www.ubervu.com/
9
• Analytics:
– Mention volume
– Sentiment
– Discussion clouds
– Activity graphs and
metrics
– Language and
geolocation filtering
– Filter by social
platform
– Comparisons

10. http://www.viralheat.com/home
• Analytics:
– Influencing users
– Sentiment and opinion analysis
– Viral content analysis
– Detecting sales leads
– Filter by geo-location

11. Tweet recipe for generating more attention
• Identifying seed posts
Top features: Time in
Day, Readability, Out-
Degree, Polarity, Informativeness
Accuracy of the classification (J48)
F1: 0.841 (User + Content)
Top features: Referral Count, Topic
Likelihood, Informativeness, Readability,
User Age
Accuracy of the classification (J48)
F1: 0.792 (User + Content + Focus)
For both datasets:
• Content features play a greater role
than user features
• The combination of all features
provides the best results
• Predicting discussion activity Top features: Referral Count(-),
Complexity(-)
User features harm the performance
Top features: Referral Count(-), Polarity(-),
Topic Likelihood(+), Complexity (+)
Best with Content +Focus
For both, a decrease in Referral Count is
associated with heightened activity.
Language and terminology are more
significant for Boards.ie.

12. Semantic engine for behaviour analysis
• Bottom Up analysis
– Every community member is
classified into a “role”
– Unknown roles might be
identified
– Copes with role changes over
time
initiators
lurkers
followers
leaders
Structural, social network,
reciprocity, persistence, participation
Feature levels change with the
dynamics of the community
Associations of roles with a collection of
feature-to-level mappings
e.g. in-degree -> high, out-degree -> high
Run rules over each user’s features
and derive the community role composition

13. Correlation of behaviour with community activity
Forum 246 – Commuting
and Transport
Forum 388 – Rugby Forum 411 – Mobile Phones and PDAs

14. Online Community Health
Analytics
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Churn Rate
FPR
TPR
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
User Count
FPR
TPR
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Seeds / Non−seeds Prop
FPR
TPR
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Clustering Coefficient
FPR
TPR
• Machine learning models to
predict community health based
on compositions and evolution
of user behaviour
Health
categories
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Seeds / Non−seeds Prop
FPR
TPR
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Clustering Coefficient
FPR
TPR
False Positive Rate
False Positive RateFalse Positive Rate
False Positive Rate
TruePositiveRateTruePositiveRate
TruePositiveRateTruePositiveRate

15. Behaviour evolution patterns
• Can we predict future
behaviour role?
• Who’s on the path to
become a leader? an
expert? a churner?
• Which users we want to
encourage staying/leaving?
experts to-be
about to churn
on right path
to leadership

16. OU Communities
• Many FB groups exist
for students of OU
courses
• Created and used by
students to discuss and
share opinions on
courses and get support
Behaviour
Analysis
Sentiment
Analysis
Topic
Analysis
Course tutors
Real time
monitoring
• How do students like
this course?
• What main topics are
they busy discussing?
• Do students get the
answers and support
they need?
• Which students are
likely to drop out?

17. What’s next!
• Community-type analysis
• Stability of results over time and events
• Health metrics (what’s good/bad?)
• Influence/change in behaviour

18. Relevant Publications
• Rowe, W. and H. Alani. What makes Communities Tick? Community Health Analysis using Role Compositions. Proceedings of
the Fourth IEEE International Conference on Social Computing. Amsterdam, The Netherlands (2012)
• Rowe, M., M Fernandez, S Angeletou and H Alani. Community Analysis through Semantic Rules and Role Composition
Derivation. In the Journal of Web Semantics (2012)
• Burel, G.; He, Y. and Alani, H. Automatic identification of best answers in online enquiry communities. In: 9th Extended
Semantic Web Conference, Crete, (2012)
• Rowe, Matthew; Fernandez, Miriam; Alani, Harith; Ronen, Inbal ; Hayes, Conor and Karnstedt, Marcel (2012). Behaviour
analysis across different types of Enterprise Online Communities. In: ACM web Science Conference 2012 (WebSci12),
Evanston, U.S.A, (2012)
• Rowe, M., Stankovic, M., and Alani, H. Who will follow whom? Exploiting semantics for link prediction in attention-
information networks. In: 11th International Semantic Web Conference (ISWC 2012), Boston, USA, (2012)
• Wagner, C., Rowe, M., Strohmaier, M. and Alani, H. Ignorance isn't bliss: an empirical analysis of attention patterns in online
communities. In: 4th IEEE International Conference on Social Computing, Amsterdam, The Netherlands, (2012)
• Angeletou, S., Rowe, M. and Alani, H. Modelling and Analysis of User Behaviour in Online Communities. International
Semantic Web Conference. Bonn, Germany (2011)
• Karnstedt, M., Rowe, M., Chan, J., Alani, H., and Hayes, C. The Effect of User Features on Churn in Social Networks. In: ACM
Web Science Conference 2011 (WebSci2011), Koblenz, Germany, (2011)
• Rowe, M., Angeletou, S., and Alani, H. Predicting discussions on the social semantic web. In: 8th Extended Semantic Web
Conference (ESWC 2011), Heraklion, Greece, (2011)
http://oro.open.ac.uk/view/person/ha2294.html