existing research and future research agenda

Lancaster-Lectureship-Existing-Future-Research-2012.pptx

EXISTING RESEARCH AND FUTURE RESEARCH AGENDA

DR MATTHEW ROWE RESEARCH ASSOCIATE KNOWLEDGE MEDIA INSTITUTE http://www.matthew-rowe.com [email protected]

The Big Picture

Dr Matthew Rowe - Existing Research and Future Research Agenda

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2006-2010: Ph.D.: Disambiguating Identity Web References using Social Data. The University of Sheffield

2010-2012: Research Associate at the Knowledge Media Institute, The Open University

Digital Identity

User Behaviour

Digital Identity Lifecycles Identity Diffusion

Time

Ph.D.2006-2010

Research Associate2010-2012

Future Work

Digital Identity Personal information is spread across the Web: (a) identity theft, (b) lateral surveillance

Identity theft costs the UK government 1.2 billion per annum (Get Safe Online, 2010)

Manually tracking web citations is time-consuming and repetitive 57% of web users perform vanity searches (Pew Internet Report, 2010)

How can identity web references be disambiguated automatically? Seed data leveraged from Social Web Systems

Information extracted from candidate citations and semantic model built

Devised three disambiguation methods that combine data mining with semantics

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Digital Identity

User Behaviour


Digital Identity Seed Data generation:

Large overlap between offline social networks and online social networks Exporting semantic social graphs from disparate social web systems (Twitter, Facebook)

Machine-readable user profile and social network information Interlinking social graphs from disparate social web systems

Disambiguation methods Rule-based: infer relations between social data and web resources Graph-based: random walks over a graph space and clustering Semi-supervised machine learning: classify web citations and learn from classifications

Findings: Social data provides necessary seed data to disambiguate web citations Achieve best performance using semi-supervised methods, outperforming several baselines

(unsupervised methods)

Rowe and Ciravegna. Disambiguating Identity Web References using Web 2.0 Data and Semantics. Journal of Web Semantics. 2010 http://www.matthew-rowe.com/?q=thesis

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Digital Identity

User Behaviour



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Attention Patterns on Social Web Systems How is user behaviour associated with heightened attention? Developed a machine learning approach to:

Identify seed posts Predict discussion lengths

User Modelling: social network properties, topical focus, community affinity

Patterns associated with increased attention: Twittter: greater broadcast spectrum Boards.ie: greater community affinity, focussed users SAP: less community messages, popular users (frequently provide answers)

Rowe et al. Anticipating Discussion Activity on Community Forums. 3rd IEEE International Conference on Social Computing, Boston, USA. 2011

Rowe et al. Predicting Discussions on the Social Semantic Web. Extended Semantic Web Conference, Heraklion, Crete. 2011

Wagner et al. What catches your attention? An empirical study of attention patterns in community forums. International Conference on Weblogs and Social Media, Dublin, Ireland. 2012

Digital Identity

User Behaviour

Digital Identity Lifecycles Identity DiffusionUser Behaviour


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Behaviour Analysis in Online Communities How can the contextual notion of behaviour be captured? What is the relation between community behaviour and health?

Modelled user behaviour along six dimensions: Focus Dispersion, Initiation, Contribution, Popularity, Engagement, Content Quality

Modelled behaviour using semantic web technologies: Behaviour Ontology capturing contextual notion of behaviour Inference rules identifying the role of a given user

Mined roles, and associated behaviour, on a given platform Correlated the time-series role composition of communities and with health indicators Found certain roles to be associated with decreases in community health

E.g. Expert Initiators linked to community churn

Rowe et al. Community Analysis through Semantic Rules and Role Composition Derivation. Journal of Web Semantics (in press). 2012

Digital Identity

User Behaviour



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Churn Churn is the loss of users from a service (telecommunications/social network,

online community) Goal: predict churners and identify churn patterns Using social network features (i.e. centrality) provided accurate information for

churn detection Found:

Differing churn patterns between communities Central users churn in some communities, while peripheral users churn in others

Currently exploring: Churn diffusion and topological effects

Karnstedt et al. The Effect of User Features on Churn in Social Networks. ACM Web Science Conference 2011, Koblenz, Germany. 2011

Digital Identity

User Behaviour


The Big Picture - Revisit

Digital Identity

User Behaviour


Time

Ph.D.2006-2010


Future Work

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Identity is developed and shaped over time through developmental stages (Eriksson, 1959)

Ego-identity is the ideal that people pursue, while identity is a persons present state (Bosma et al., 1994)

How are digital identities shaped online? Do the stages resonate with Eirkssons theories?

What development stages do they go through? Is there a common life cycle across systems? In role analysis there are common transitions from one role to another

What are the motivations behind digital identity formation and amendments? Self-efficacy Self-affirmation

Understanding identity lifecycles leads to: Better recommendations (followees, products, content) Tracking of disseminated personal information Identifying users susceptible to stealing reality attacks (Altshuler et al., 2011)

Digital Identity

User Behaviour

Digital Identity Lifecycles Identity DiffusionIdentity Lifecycles


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Identity Diffusion is the propagation of identity attributes through social systems I.e. the adoption of defining characteristics from neighbours

What network effects are associated with identity diffusion? Small core of central users found to be influential in protest recruitment (Gonzalez-Bailon et al., 2011) Core web sites found to influence the spread of memes (Gomez-Rodriguez et al., 2012)

What is the role of passive/active networks on identity formation? Behaviour adoption is maintained through social reinforcement (Centola, 2010)

Local-level influence (i.e. homophily, inequity, balancing) Weak-tied individuals in ego-networks influence adoption (Garg et al., 2011) Inverse correlation between node influence and degree (Katona et al., 2011)

What effects do community actions have on web presence and subscriber churn? Online community churn (Karnstedt et al., 2010), (Zhang et al., 2010), (Kawale et al., 2010) Recently studied in the context of ego-networks (Quercia et al., 2012), (Kwak et al., 2011)

Understanding and modelling identity diffusion leads to: Identification of links between behaviour and churn from online systems Enable understanding of reductions in web presence (Online marketing, brand promotion)

Digital Identity

User Behaviour

Digital Identity Lifecycles Identity DiffusionIdentity Diffusion

The Big Picture - Recap

Digital Identity

User Behaviour


Time

Ph.D.2006-2010


Future Work

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http://www.matthew-rowe.com [email protected]

Questions? 11


existing research and future research agenda

Education