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Generating and Tracking Communities Based on Implicit Affinities

Generating and Tracking Communities Based on Implicit Affinities. Matthew Smith – smitty@byu.edu BYU Data Mining Lab April 2007. Introduction. Online Communities Continually emerging – many sites are adding this aspect Like offline communities, they are complex and dynamic Examples

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Generating and Tracking Communities Based on Implicit Affinities

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  1. Generating and Tracking Communities Based on Implicit Affinities Matthew Smith – smitty@byu.edu BYU Data Mining Lab April 2007

  2. Introduction • Online Communities • Continually emerging – many sites are adding this aspect • Like offline communities, they are complex and dynamic • Examples • USENET (1980), Google Groups, Wikipedia • LinkedIn, Flickr, YouTube, MySpace, Facebook, etc. • Medical Communities (e.g., DailyStrength, NAAF) • Political Communities • Blogosphere – focus of experiments

  3. Motivation Explicit Links Explicit Social Network (ESN) Links: Friends, Web Links, etc.

  4. Motivation Explicit Links Implicit Affinities cancer bald smoke ESN and Implicit Affinity Network (IAN) Applications: Medical, Blogosphere, etc.

  5. Implicit Affinity • Affinity: • The overlapping of attributes-values for any common attribute • Community: • Set of individuals characterized by attributes • Linked by affinities rather than explicit relationships

  6. IAN Community Generation • Individuals – nodes • characterized by attributes • Affinities – edges • unlike traditional social networks where links represent explicit relationships, the links in our approach are based strictly on affinities • Connections emerge naturally

  7. Affinity Scoring Affinity score for a particular attribute Affinity score for all attributes

  8. Affinity Network Building IAN

  9. Social Capital for Community Tracking Social Capital: The advantage available through connections between individuals within a particular network Bonding and Bridging Metrics

  10. Preliminary Experiments & Observations

  11. Scobleizer’s Blog List • Robert Scoble (“Scobleizer”) • Blogger and book author • Technical evangelist (formerly with Microsoft) • Data Set Details: • Scobleizer’s reading list at Bloglines.com • 570 blogs • 2380 bloggers

  12. Data Set Statistics – Blog posts per day Lack of data for all bloggers during first few days We observe fewer posts during the weekend (Friday & Saturday)

  13. Single Attribute: Companies • Motivation • Many bloggers talk about various companies and what they are doing • Methodology • Whenever a company is mentioned in a blogger’s post, it becomes a feature of the blogger • Static company list used as attributes • 1,914 company names

  14. Cyclic Feature Usage

  15. Power-law Behavior – Features Observations • Few companies • mentioned by many • Many companies • mentioned by few

  16. Blog Community Evolution Observations • Weekend bonding? • Bridging indicates • newly used features • new bloggers • Overall bonding (expected) • static set of features • no decay • blogosphere is full of buzz

  17. Blog-based IAN – Feb. 24 niche sub-communities exist

  18. Conclusions • Blog posts were cyclic within this community • Posted more during the week and less during the weekends • Interestingly, bonding occurs during the weekends • Companies were mentioned in a power-law way • Few companies are mentioned often • Most companies are mentioned rarely • Niche sub-communities • Bloggers focusing on long-tail companies were identified • Blog-based IAN • Appears to follow power-law connectivity like ESNs

  19. Future Work (In Progress) • Compare IAN and ESN of the same community • Analyze evolution (social capital vs. density) • Compare snapshots • Identify and report similarities and differences • Develop hybrid sub-community identification • Experiment on domain-specific communities • Medical – patient communities • Political – jump start grass-roots campaigns

  20. More Future Work • Refine implicit attribute extraction • Allow for dynamic feature extraction • Allow features to naturally decay with time • Use LDA to extract “concepts” • Putnam’s puzzle • Consider adapting Social Capital measures to allow for uncorrelated bonding and bridging

  21. Questions ?

  22. Affinity Score Distribution

  23. Blog-based IANs– Filtered by Threshold Affinity Scores GTE 0.5 Affinity Score of 1.0

  24. Blog-based IAN – Filtered by Thresholds Affinity Thresholds Score GTE 0.5Count GTE 3 2/15 – 3/15

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