Lecture4 Social Web


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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.

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Lecture4 Social Web

  1. 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. 2. Why?• UCG provides an enormous wealth of data • insights in users’ daily lives • insights in communities • insights in trends
  3. 3. To whom it may concern• Politicians• Companies• Governmental institutions• You?
  4. 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. 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. 6. What do you prefer? Image: http://www.co.olmsted.mn.us/prl/propertyrecords/RecordingDocuments/PublishingImages/forms.jpg
  7. 7. The Rise of the Data Scientist http://radar.oreilly.com/2010/06/what-is-data-science.html
  8. 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. 9. Popular Data Products
  10. 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. 11. Data Mining 101Databases Statistics Artificial Intelligence
  12. 12. Steps• Data input & exploration• Preprocessing• Data mining algorithms• Evaluation & Interpretation
  13. 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. 14. Input & Exploration in ‘LikeMiner’
  15. 15. Preprocessing• Cleanup!• Choose a suitable data model • What happens if you integrate data from multiple sources?• Reformat your data
  16. 16. Preprocessing in ‘LikeMiner’
  17. 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. 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. 19. Interpreting your results
  20. 20. Data Mining is not easy
  21. 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. 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. 23. Populations http://www.brandrants.com/brandrants/obama/
  24. 24. Brand Sentiment via Twitterhttp://flowingdata.com/2011/07/25/brand-sentiment-showdown/
  25. 25. Recommended Readinghttp://www.cs.cornell.edu/home/kleinber/ networks-book/networks-book.pdf
  26. 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. 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/