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Behaviour and Health Analysis of Online Communities
Harith AlaniKnowledge 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
Milton Keynes
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
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
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/
“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/
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
Tools for monitoring social networks
http://www.ubervu.com/9
• Analytics: – Mention volume
– Sentiment
– Discussion clouds
– Activity graphs and
metrics
– Language and
geolocation filtering
– Filter by social
platform
– Comparisons
http://www.viralheat.com/home
• Analytics: – Influencing users
– Sentiment and opinion analysis
– Viral content analysis
– Detecting sales leads
– Filter by geo-location
Tweet recipe for generating more attention• Identifying seed posts
Tw
itte
rB
oa
rds.
ie
Top features: Time in Day, Readability, Out-Degree, Polarity, InformativenessAccuracy of the classification (J48) F1: 0.841 (User + Content)
Top features: Referral Count, Topic Likelihood, Informativeness, Readability, User AgeAccuracy 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 resultsTw
itte
r vs
. B
oa
rds.
ie
• 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.
Tw
itte
rB
oa
rds.
ieT
witt
er
vs.
Bo
ard
s.ie
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 mappingse.g. in-degree -> high, out-degree -> high
Run rules over each user’s features and derive the community role composition
Correlation of behaviour with community activity
Forum 246 – Commuting and Transport
Forum 388 – Rugby Forum 411 – Mobile Phones and PDAs
Online Community Health Analytics
• Machine learning models to predict community health based on compositions and evolution of user behaviour
Health categories
False Positive Rate
False Positive RateFalse Positive Rate
False Positive Rate
True
Pos
itive
Rat
eTr
ue P
ositi
ve R
ate
True
Pos
itive
Rat
eTr
ue P
ositi
ve R
ate
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
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?
What’s next!
• Community-type analysis• Stability of results over time and events• Health metrics (what’s good/bad?)• Influence/change in behaviour
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