Lecture 4: How can we MINE, ANALYSE & VISUALISE the Social Web? (2014)

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

BIG Data Challenges


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

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?

• ..…

This doesn’t work

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


How about this?


Who uses it?


Politicians
Governmental
institutions


Whole
society


Whole
society

repurposing
data
danger of
second order
effect


Whole
society

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


Scientists

Bibliometrics


Culture
History


Culture

Bill Howe, University of Washington


Entertainment


You?


Companies


Who does it?


The Rise of the Data Scientist



Data Geeks Skills:
Statistics
Data munging
Visualisation


http://radar.oreilly.com/2010/06/what-is-data-science.html


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.


Data Science Venn Diagram

Drew Conway


Popular Data Products

Data Science is about

building products

not just answering questions

Popular Data Products
empower the others
to their own analysis

empower the others to
use the data

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


• 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”

Preprocessing

“LikeMiner”
• Cleanup!

• Choose a suitable data model

• What happens if you integrate data from multiple sources?

• Reformat your data

Data Mining Algorithms
• Classiﬁcation: Generalising a known structure &
apply to new data

• Association: Finding relationships between
variables

• Clustering: Discovering groups and structures in
data


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


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


Data Mining is not easy


Data Journalism


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


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


Populations

http://www.brandrants.com/brandrants/obama/

Brand Sentiment via Twitter

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


Sentiment Analysis as Service


http://text-processing.com/demo/sentiment/


Recommended Reading

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


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

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/

Lecture 4: How can we MINE, ANALYSE & VISUALISE the Social Web? (2014)

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