Lecture 4: How do we MINE, ANALYSE & VISUALISE the Social Web? (VU Amsterdam Social Web Course)
1. Social Web
2015
Lecture 4: How do we MINE, ANALYSE VISUALISE
the Social Web?
Anca Dumitrache Lora Aroyo
The Network Institute
VU University Amsterdam
2. • 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
The Age of BIG Data
Social Web 2015, Lora Aroyo
5. enormous wealth of data = lots of insights
• insights in users’ daily lives and activities
• insights in history
• insights in politics
• insights in communities
• insights in trends
• insights in businesses brands
Why?
Social Web 2015, Lora Aroyo
6. enormous wealth of data = lots of insights
• who uploads/talks? (age, gender, nationality,
community, etc.)
• what are the trending topics? when?
• what else do these users like? on which platform?
• who are the most/least active users?
• ..…
Why?
Social Web 2015, Lora Aroyo
24. • 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.
Data Science
Social Web 2015, Lora Aroyo
27. Popular Data Products
Data Science is about
building products
not just answering questions
Social Web 2015, Lora Aroyo
28. Popular Data Products
empower the others to
use the data
empower the others
to their own analysis
Social Web 2015, Lora Aroyo
29. (Inspired by George Tziralis’ FOSS Conf’09, John Elder IV’s Salford Systems Data
Mining Conf. and Toon Calders’ slides)
Data mining is the exploration analysis of
large quantities of data
in order to discover valid, novel, potentially useful,
ultimately understandable patterns in data
http://www.freefoto.com/images/33/12/33_12_7---Pebbles_web.jpg
Data Mining 101
Social Web 2015, Lora Aroyo
31. • What data do I
need to answer
question X?
• What variables
are in the data?
• Basic stats of my
data?
Data Input Exploration
“LikeMiner”
Social Web 2015, Lora Aroyo
32. • Cleanup!
• Choose a suitable data model
• What happens if you integrate data from multiple sources?
• Reformat your data
Preprocessing
“LikeMiner”
Social Web 2015, Lora Aroyo
33. • Classification: Generalising a known structure
apply to new data
• Association: Finding relationships between
variables
• Clustering: Discovering groups and structures in
data
Data Mining Algorithms
Social Web 2015, Lora Aroyo
34. • 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
Mining in “LikeMiner”
Social Web 2015, Lora Aroyo
35. Evaluation Interpretation
What does the pattern I found mean?!
• Pitfalls:
• Meaningless Discoveries
• Implication ≠ Causality (Intensive care - death)
• Simpson’s paradox
• Data Dredging
• Redundancy
• No New Information
• Overfitting
• Bad Experimental Setup
Social Web 2015, Lora Aroyo
47. http://www.actmedia.eu/media/img/text_zones/English/small_38421.jpg
Assignment 2: Semantic Markup
• Part I: enrich/create a Web page with semantic markup!
• Step 1: Mark up two different Web pages with the appropriate markup describing properties of
at least people, relationships to other people, locations, some temporally related data and
some multimedia. You can also try out tools such as Google Markup Helper
• Step 2: Validate your semantic markup. Use existing validator.
• Step 3: Explain why you chose particular markups. Compare the advantages and disadvantages of
the different markups. Include screenshots from validators.
• Part II: analyse other team’s Web page markup - as a consumer as a publisher!
• Step 1: Perform evaluation and report your findings (consider findability or content extraction)
• Step 2: Support your critique with examples of how the semantic markup could be improved.
• In introductory section explain what semantic markup is, what it is for, what it looks like etc.
• Support your choices and explanations with appropriate literature references.
• 5 pages (excluding screen shots).
• Other group’s evaluation details in appendix.
• Deadline: 3 March 23:59!
48. Image Source: http://blog.compete.com/wp-content/uploads/2012/03/Like.jpg
Final Assignment:
Your SocWeb App
• Create your own Social Web app (in a group)
• Use structured data, entity relations, data analysis, visualisation
• Write individual report on one of the main aspects of your app
• Pitch your app idea before finalising: 12 Mar, during Hands-on
• Submit final assignment : 27 March 23:59
Social Web 2015, Lora Aroyo