1. Selver Softic
Social Learning
TOWARDS IDENTIFYING COLLABORATIVE
LEARNING GROUPS USING SOCIAL MEDIA

2. Agenda
• Motivation
• Problem statement
• Methodology
• Concept
• Implementation
• Evaluation
• Conclusion and future work

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. 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. 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. 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. Methodology
• Basic metrics
– #hashtags
– @mentions
– occurrence
• Evaluation tools:
– Cosine Similarity, Euclidian Distance, Thresholds
• Focus on relevant information carriers

8. Concept: interest group
G(i)
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H α
α
δ
α
I(i)

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. Implementation

11. Implementations ctd.
• Similarity API
– user to user
– user to user group
• user grou can be randomised

12. Evaluation

13. Evaluation

14. Evaluation

15. Evaluation

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. Contact
Twitter:
@selvers
Mail:
selver.softic@tugraz.at
Slideshare:
selvos
Linkedin:
http://at.linkedin.com/pub/selver-softic/24/33b/211