TOWARDS IDENTIFYING COLLABORATIVE LEARNING GROUPS USING SOCIAL MEDIA

Selver SofticSocial Learning

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!

Methodology

• Basic metrics– #hashtags– @mentions– occurrence

• Evaluation tools: – Cosine Similarity, Euclidian Distance, Thresholds

• Focus on relevant information carriers

Concept: interest group

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I(i)

G(i)

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Implementation• Reference source

– Grabeeter database– 1600 users– approx. 4,7 million tweets

• 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%

http://grabeeter.tugraz.at/

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– 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

Contact

Twitter: @selvers

Mail: selver.softic@tugraz.at

Slideshare:selvos

Linkedin:http://at.linkedin.com/pub/selver-softic/24/33b/211