Selver SofticSocial LearningTOWARDS IDENTIFYING COLLABORATIVELEARNING GROUPS USING SOCIAL MEDIA
Agenda• Motivation• Problem statement• Methodology• Concept• Implementation• Evaluation• Conclusion and future work
Motivation• Web 2.0• User generated content• Social Networks• Microblogging• Twitter http://blog.socialmaximizer.com/wp-content/uploads/2012/09/Social-Media.jpg
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/
Motivation ctd.• Huge amount of informations• Sharing of interests, experiences etc.• no cultural or georgraphical boundaries• Implicit knowledge• Appliances: conferences, course support, viral marketing
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!
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