Your SlideShare is downloading. ×
Lecture4 Social Web
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Lecture4 Social Web

509
views

Published on

How can we mine, analyse and visualise the Social Web? …

How can we mine, analyse and visualise the Social Web?
In this lecture, you will learn about mining social web data for analysis. Data preparation and gathering basic statistics on your data.

Published in: Education

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
509
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Social Web Lecture 4How can we MINE, ANALYSE and VISUALISE the Social Web? (1) Marieke van Erp The Network Institute VU University Amsterdam
  • 2. Why?• UCG provides an enormous wealth of data • insights in users’ daily lives • insights in communities • insights in trends
  • 3. To whom it may concern• Politicians• Companies• Governmental institutions• You?
  • 4. The Age of Big Data• 25 billion tweets on Twitter in 2010, by 175 million users• 360 billion pieces of contents on Facebook in 2010, by 600 million different users• 35 hours of videos uploaded to YouTube every minute• 130 million photos uploaded to flickr per month
  • 5. Questions to Ask• Who uploads/talks? (age, gender, nationality, community)• What are the trending topics?• What else do these users like?• Who are the most/least active users?• etc.
  • 6. What do you prefer? Image: http://www.co.olmsted.mn.us/prl/propertyrecords/RecordingDocuments/PublishingImages/forms.jpg
  • 7. The Rise of the Data Scientist http://radar.oreilly.com/2010/06/what-is-data-science.html
  • 8. The Rise of the Data Scientist• Data Science enables the creation of data products• Data products are applications that acquire their value from the data, and create more data as a result.• Users are in a feedback loop: they constantly provide information about the products they use, which gets used in the data product.
  • 9. Popular Data Products
  • 10. Data Mining 101 Data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data. (Inspired by George Tziralis’ FOSS Conf’09, John Elder IV’sSalford Systems Data Mining Conf. and Toon Calders’ slides) http://www.freefoto.com/images/33/12/33_12_7---Pebbles_web.j
  • 11. Data Mining 101Databases Statistics Artificial Intelligence
  • 12. Steps• Data input & exploration• Preprocessing• Data mining algorithms• Evaluation & Interpretation
  • 13. Data Input & Exploration• What data do I need to answer question X?• What variables are in the data?• Basic stats of my data?
  • 14. Input & Exploration in ‘LikeMiner’
  • 15. Preprocessing• Cleanup!• Choose a suitable data model • What happens if you integrate data from multiple sources?• Reformat your data
  • 16. Preprocessing in ‘LikeMiner’
  • 17. Data mining algorithms• Classification: Generalising a known structure & apply to new data• Association: Finding relationships between variables• Clustering: Discovering groups and structures in data
  • 18. Mining in ‘LikeMiner’• Filter users by interests• Construct user graphs• PageRank on graphs to mine representativeness• Result: set of influential users• Compare page topics to user interests to find pages most representative for topics
  • 19. Interpreting your results
  • 20. Data Mining is not easy
  • 21. Mining Social Web Data source: http://kunau.us/wp-content/uploads/ 2011/02/Screen-shot-2011-02-09- at-9.03.46-PM-w600-h900.png
  • 22. Single Person Source: http://infosthetics.com/archives/2011/12/ all_the_information_facebook_knows_about_you.html See also: http://www.youtube.com/watch?feature=player_embedded&v=kJvAUqs3Ofg
  • 23. Populations http://www.brandrants.com/brandrants/obama/
  • 24. Brand Sentiment via Twitterhttp://flowingdata.com/2011/07/25/brand-sentiment-showdown/
  • 25. Recommended Readinghttp://www.cs.cornell.edu/home/kleinber/ networks-book/networks-book.pdf
  • 26. Final Assignment:Your SocWeb App • Create a Social Web app with your group • Use structured data, relationships between entities, data analysis, visualisation • Write individual research report on one of the main aspects of your app Image Source: http://blog.compete.com/wp-content/uploads/2012/03/Like.jpg
  • 27. Hands-on Teaser• Build your own recommender system 101• Recommend pages on del.icio.us• Recommend pages to your Facebook friends image source: http://www.flickr.com/photos/bionicteaching/1375254387/