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Lecture4 Social Web


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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.

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  • 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:
  • 7. The Rise of the Data Scientist
  • 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)
  • 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: 2011/02/Screen-shot-2011-02-09- at-9.03.46-PM-w600-h900.png
  • 22. Single Person Source: all_the_information_facebook_knows_about_you.html See also:
  • 23. Populations
  • 24. Brand Sentiment via Twitter
  • 25. Recommended Reading 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:
  • 27. Hands-on Teaser• Build your own recommender system 101• Recommend pages on• Recommend pages to your Facebook friends image source: