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Social Web
2014
Lecture IV: How can we MINE, ANALYSE &
the Social Web? (1)
Lora Aroyo
The Network Institute	

VU University Amsterdam

Social Web 2014, Lora Aroyo!
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
Social Web 2014, Lora Aroyo!
Science with BIG Data
BIG Data Challenges

Social Web 2014, Lora Aroyo!
Why?
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
Social Web 2014, Lora Aroyo!
Why?
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?	

• ..…
Social Web 2014, Lora Aroyo!
This doesn’t work

Image: http://www.co.olmsted.mn.us/prl/
propertyrecords/RecordingDocuments/
PublishingImages/forms.jpg

Social Web 2014, Lora Aroyo!
How about this?

Social Web 2014, Lora Aroyo!
Who uses it?

Social Web 2014, Lora Aroyo!
Politicians
Governmental
institutions

Social Web 2014, Lora Aroyo!
Whole
society

Social Web 2014, Lora Aroyo!
Whole
society

repurposing
data
danger of
second order
effect

Social Web 2014, Lora Aroyo!
Whole
society

repurposing
data
danger of
second order
effect

Social Web 2014, Lora Aroyo!
Whole
society

repurposing
data
discoveries &
correlations
Web-Scale Pharmacovigilance: Listening to Signals from the Crowd, R.W. White et al (2013)	

Social Web 2014, Lora Aroyo!
Whole
society

repurposing
data
discoveries &
correlations
Web-Scale Pharmacovigilance: Listening to Signals from the Crowd, R.W. White et al (2013)	

Social Web 2014, Lora Aroyo!
Whole
society

repurposing
data
discoveries &
correlations
Web-Scale Pharmacovigilance: Listening to Signals from the Crowd, R.W. White et al (2013)	

Social Web 2014, Lora Aroyo!
Scientists

Bibliometrics

Social Web 2014, Lora Aroyo!
Scientists

Bibliometrics

Social Web 2014, Lora Aroyo!
Scientists

Bibliometrics

Social Web 2014, Lora Aroyo!
Culture
History

Social Web 2014, Lora Aroyo!
Culture
History

Social Web 2014, Lora Aroyo!
Culture
History

Social Web 2014, Lora Aroyo!
Culture
History

Social Web 2014, Lora Aroyo!
Culture
History

Social Web 2014, Lora Aroyo!
Culture

Bill Howe, University of Washington

Social Web 2014, Lora Aroyo!
Entertainment

Social Web 2014, Lora Aroyo!
Entertainment

Social Web 2014, Lora Aroyo!
Entertainment

Social Web 2014, Lora Aroyo!
You?

Social Web 2014, Lora Aroyo!
You?

Social Web 2014, Lora Aroyo!
Companies

Social Web 2014, Lora Aroyo!
Who does it?

Social Web 2014, Lora Aroyo!
The Rise of the Data Scientist

Social Web 2014, Lora Aroyo!
The Rise of the Data Scientist

Social Web 2014, Lora Aroyo!
The Rise of the Data Scientist

Social Web 2014, Lora Aroyo!
The Rise of the Data Scientist

Social Web 2014, Lora Aroyo!
The Rise of the Data Scientist

Data Geeks Skills:
Statistics
Data munging
Visualisation

Social Web 2014, Lora Aroyo!
The Rise of the Data Scientist
http://radar.oreilly.com/2010/06/what-is-data-science.html

Social Web 2014, Lora Aroyo!
Data Science
• 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.

Social Web 2014, Lora Aroyo!
Data Science Venn Diagram

Drew Conway

Social Web 2014, Lora Aroyo!
Social Web 2014, Lora Aroyo!
Popular Data Products

Data Science is about 	

building products 	

not just answering questions
Social Web 2014, Lora Aroyo!
Popular Data Products
empower the others
to their own analysis

empower the others to
use the data
Social Web 2014, Lora Aroyo!
Data Mining 101

Data mining is the exploration & analysis of
large quantities of data
in order to discover valid, novel, potentially useful,
& 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)

Social Web 2014, Lora Aroyo! http://www.freefoto.com/images/33/12/33_12_7---Pebbles_web.jpg
Data Mining 101

Databases

Statistics

Artificial 	

Intelligence

Social Web 2014, Lora Aroyo!

• Data input &
exploration	

• Preprocessing	

• Data mining
algorithms	

• Evaluation &
Interpretation
Data Input & Exploration
• What data do I
need to answer
question X?	

• What variables
are in the data?	

• Basic stats of my
data?
“LikeMiner”
Social Web 2014, Lora Aroyo!
Preprocessing

“LikeMiner”
• Cleanup! 	

• Choose a suitable data model	

• What happens if you integrate data from multiple sources?	

• Reformat your data
Social Web 2014, Lora Aroyo!
Data Mining Algorithms
• Classification: Generalising a known structure &
apply to new data 	

• Association: Finding relationships between
variables	

• Clustering: Discovering groups and structures in
data

Social Web 2014, Lora Aroyo!
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

Social Web 2014, Lora Aroyo!
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 2014, Lora Aroyo!
Data Mining is not easy

Social Web 2014, Lora Aroyo!
Data Journalism

Social Web 2014, Lora Aroyo!
Social Web 2014, Lora Aroyo!
Mining Social Web Data

source: http://kunau.us/wp-content/uploads/
2011/02/Screen-shot-2011-02-09at-9.03.46-PM-w600-h900.png

Social Web 2014, Lora Aroyo!
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

Social Web 2014, Lora Aroyo!
Populations

http://www.brandrants.com/brandrants/obama/
Social Web 2014, Lora Aroyo!
Brand Sentiment via Twitter

http://flowingdata.com/2011/07/25/brand-sentiment-showdown/

Social Web 2014, Lora Aroyo!
Sentiment Analysis as Service

Social Web 2014, Lora Aroyo!
http://text-processing.com/demo/sentiment/

Social Web 2014, Lora Aroyo!
Recommended Reading

http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf

Social Web 2014, Lora Aroyo!
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: 4 March 23:59
Social Web 2014, Lora Aroyo!

http://www.actmedia.eu/media/img/text_zones/English/small_38421.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: 13 March, during Hands-on	

Submit: 28 March 23:59

Social WebImage Lora Aroyo!
2014, Source: http://blog.compete.com/wp-content/uploads/2012/03/Like.jpg
Hands-on Teaser
• Build your own recommender system 101	

• Recommend pages on del.icio.us 	

• Recommend pages to your Facebook friends

image
Social Web 2014, Lora Aroyo! source: http://www.flickr.com/photos/bionicteaching/1375254387/

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