Selver SofticSocial LearningTOWARDS IDENTIFYING COLLABORATIVELEARNING GROUPS USING SOCIAL MEDIA
Agenda•   Motivation•   Problem statement•   Methodology•   Concept•   Implementation•   Evaluation•   Conclusion and futu...
Motivation•   Web 2.0•   User generated content•   Social Networks•   Microblogging•   Twitter                         htt...
Motivation•   57% of people talk to people more online than they do in real life•   40% of Twitter users don’t tweet, but ...
Motivation ctd.•   Huge amount of informations•   Sharing of interests, experiences etc.•   no cultural or georgraphical b...
Problem statement• Cluster users into sub-networks based upon their interest  using topic items and social relations• Prov...
Methodology• Basic metrics   – #hashtags   – @mentions   – occurrence• Evaluation tools:   – Cosine Similarity, Euclidian ...
Concept: interest group                           G(i)                                      tc,tl               H         ...
Implementation• Reference source   – Grabeeter database   – 1600 users   – approx. 4,7 million tweets           http://gra...
Implementation
Implementations ctd.• Similarity API  – user to user  – user to user group     • user grou can be randomised
Evaluation
Evaluation
Evaluation
Evaluation
Conclusion and future work• Results encouraging but:  – More accurate and qualitative evaluation of    clustering  – Invol...
Contact                 Twitter:                 @selvers                  Mail:              selver.softic@tugraz.at     ...
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Towards identifying Collaborative Learning groups using Social Media

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Towards identifying Collaborative Learning groups using Social Media

  1. 1. Selver SofticSocial LearningTOWARDS IDENTIFYING COLLABORATIVELEARNING GROUPS USING SOCIAL MEDIA
  2. 2. Agenda• Motivation• Problem statement• Methodology• Concept• Implementation• Evaluation• Conclusion and future work
  3. 3. Motivation• Web 2.0• User generated content• Social Networks• Microblogging• Twitter http://blog.socialmaximizer.com/wp-content/uploads/2012/09/Social-Media.jpg
  4. 4. Motivation• 57% of people talk to people more online than they do in real life• 40% of Twitter users don’t tweet, but instead use it to keep up to date• A great majority of tweets are just 40 characters long• Social media use is becoming much more even across age groups (see graph below) http://thesocialskinny.com/100-social-media-statistics-for-2012/
  5. 5. Motivation ctd.• Huge amount of informations• Sharing of interests, experiences etc.• no cultural or georgraphical boundaries• Implicit knowledge• Appliances: conferences, course support, viral marketing
  6. 6. Problem statement• Cluster users into sub-networks based upon their interest using topic items and social relations• Provide a filtered view on information generated in their micro sub-networks• Which methods or technologies would be suitable for this challenge?• Define and evaluate the metrics that can be used to achieve this goal!
  7. 7. Methodology• Basic metrics – #hashtags – @mentions – occurrence• Evaluation tools: – Cosine Similarity, Euclidian Distance, Thresholds• Focus on relevant information carriers
  8. 8. Concept: interest group G(i) tc,tl H α α δ α I(i)
  9. 9. Implementation• Reference source – Grabeeter database – 1600 users – approx. 4,7 million tweets http://grabeeter.tugraz.at/• Reference data base – 100 users talking on term „e-learning“ – always last 250 hundred tweets considered• Verfication account• Scaling the input vectors• Thresholds: 10% and 20%
  10. 10. Implementation
  11. 11. Implementations ctd.• Similarity API – user to user – user to user group • user grou can be randomised
  12. 12. Evaluation
  13. 13. Evaluation
  14. 14. Evaluation
  15. 15. Evaluation
  16. 16. Conclusion and future work• Results encouraging but: – More accurate and qualitative evaluation of clustering – Involving other methods Pearson, Jaccard – Extending the measurement on more appliance cases and reference users regarding the collaborative learning issues• Later: k-means, hierarchical clustering
  17. 17. Contact Twitter: @selvers Mail: selver.softic@tugraz.at Slideshare: selvos Linkedin:http://at.linkedin.com/pub/selver-softic/24/33b/211

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