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