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Lecture 5: Social Web Data Analysis (2012)

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Lora Aroyo
Lora Aroyo
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Social Web Lecture 5 How can we MINE, ANALYSE and VISUALISE the Social Web? (1) Marieke van Erp The Network Institute VU University Amsterdam Monday, March 5, 12
Why? • UCG provides an enormous wealth of data • insights in users’ daily lives • insights in communities • insights in trends Monday, March 5, 12
What’s the added value of mining social web data for the individual? Monday, March 5, 12
To whom it may concern • Politicians • Companies • Governmental institutions • You? Monday, March 5, 12
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 Monday, March 5, 12
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. Monday, March 5, 12
The Rise of the Data Scientist http://radar.oreilly.com/2010/06/what-is-data-science.html Monday, March 5, 12
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. Monday, March 5, 12
Popular Data Products Monday, March 5, 12
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’s Salford Systems Data Mining Conf. and Toon Calders’ slides) http://www.freefoto.com/images/33/12/33_12_7---Pebbles_web.j Monday, March 5, 12
Data Mining 101 Databases Statistics Artificial Intelligence Monday, March 5, 12
Steps • Data input & exploration • Preprocessing • Data mining algorithms • Evaluation & Interpretation Monday, March 5, 12
Data Input & Exploration • What data do I need to answer question X? • What variables are in the data? • Basic stats of my data? Monday, March 5, 12
Are all likes equal? Do they all mean the same? Do people like for the same reason? The ‘likes’ across the different systems? Monday, March 5, 12
Input & Exploration in ‘LikeMiner’ Monday, March 5, 12
Preprocessing • Cleanup! • Choose a suitable data model • What happens if you integrate data from multiple sources? • Reformat your data Monday, March 5, 12
Preprocessing in ‘LikeMiner’ Monday, March 5, 12
Data mining algorithms • Classification: Generalising a known structure & apply to new data • Association: Finding relationships between variables • Clustering: Discovering groups and structures in data Monday, March 5, 12
How do you know you measured what you wanted to measure? Monday, March 5, 12
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 Monday, March 5, 12
Interpreting your results Monday, March 5, 12
Data Mining is not easy Monday, March 5, 12
Monday, March 5, 12
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 Monday, March 5, 12
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 Monday, March 5, 12
Populations http://www.brandrants.com/brandrants/obama/ Monday, March 5, 12
Brand Sentiment via Twitter http://flowingdata.com/2011/07/25/brand-sentiment-showdown/ Monday, March 5, 12
Assignment 3: Data Analysis • Analyse an existing social data analysis report • Apply same analyses to your own data • Write research report http://www.actmedia.eu/media/img/text_zones/English/small_38421.jpg Monday, March 5, 12
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 Monday, March 5, 12
Hands-on Teaser • Your Facebook Friends’ popularity in a spread sheet • Locations of your Facebook Friends • Tag Cloud of your wall posts image source: http://www.flickr.com/photos/bionicteaching/1375254387/ Monday, March 5, 12

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Lecture 5: Social Web Data Analysis (2012)

  • 1. Social Web Lecture 5 How can we MINE, ANALYSE and VISUALISE the Social Web? (1) Marieke van Erp The Network Institute VU University Amsterdam Monday, March 5, 12
  • 2. Why? • UCG provides an enormous wealth of data • insights in users’ daily lives • insights in communities • insights in trends Monday, March 5, 12
  • 3. What’s the added value of mining social web data for the individual? Monday, March 5, 12
  • 4. To whom it may concern • Politicians • Companies • Governmental institutions • You? Monday, March 5, 12
  • 5. 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 Monday, March 5, 12
  • 6. 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. Monday, March 5, 12
  • 7. The Rise of the Data Scientist http://radar.oreilly.com/2010/06/what-is-data-science.html Monday, March 5, 12
  • 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. Monday, March 5, 12
  • 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’s Salford Systems Data Mining Conf. and Toon Calders’ slides) http://www.freefoto.com/images/33/12/33_12_7---Pebbles_web.j Monday, March 5, 12
  • 11. Data Mining 101 Databases Statistics Artificial Intelligence Monday, March 5, 12
  • 12. Steps • Data input & exploration • Preprocessing • Data mining algorithms • Evaluation & Interpretation Monday, March 5, 12
  • 13. Data Input & Exploration • What data do I need to answer question X? • What variables are in the data? • Basic stats of my data? Monday, March 5, 12
  • 14. Are all likes equal? Do they all mean the same? Do people like for the same reason? The ‘likes’ across the different systems? Monday, March 5, 12
  • 15. Input & Exploration in ‘LikeMiner’ Monday, March 5, 12
  • 16. Preprocessing • Cleanup! • Choose a suitable data model • What happens if you integrate data from multiple sources? • Reformat your data Monday, March 5, 12
  • 17. Preprocessing in ‘LikeMiner’ Monday, March 5, 12
  • 18. Data mining algorithms • Classification: Generalising a known structure & apply to new data • Association: Finding relationships between variables • Clustering: Discovering groups and structures in data Monday, March 5, 12
  • 19. How do you know you measured what you wanted to measure? Monday, March 5, 12
  • 20. 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 Monday, March 5, 12
  • 21. Interpreting your results Monday, March 5, 12
  • 22. Data Mining is not easy Monday, March 5, 12
  • 24. 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 Monday, March 5, 12
  • 25. 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 Monday, March 5, 12
  • 26. Populations http://www.brandrants.com/brandrants/obama/ Monday, March 5, 12
  • 27. Brand Sentiment via Twitter http://flowingdata.com/2011/07/25/brand-sentiment-showdown/ Monday, March 5, 12
  • 28. Assignment 3: Data Analysis • Analyse an existing social data analysis report • Apply same analyses to your own data • Write research report http://www.actmedia.eu/media/img/text_zones/English/small_38421.jpg Monday, March 5, 12
  • 29. 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 Monday, March 5, 12
  • 30. Hands-on Teaser • Your Facebook Friends’ popularity in a spread sheet • Locations of your Facebook Friends • Tag Cloud of your wall posts image source: http://www.flickr.com/photos/bionicteaching/1375254387/ Monday, March 5, 12