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

Social Web 2014, Lora Aroyo!

Lecture IV: How can we MINE, ANALYSE &the Social Web? (1)

Lora Aroyo The Network Institute

VU University Amsterdam

Social Web 2014

• 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

Science with BIG Data


BIG Data Challenges

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? • ..…


Why?

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

PublishingImages/forms.jpg


This doesn’t work

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



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

• 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


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 use the data

empower the others to their own analysis

(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.jpgSocial Web 2014, Lora Aroyo!

Data Mining 101

http://www.freefoto.com/images/33/12/33_12_7---Pebbles_web.jpg

Databases Statistics

Artificial Intelligence


Data Mining 101

• Data input & exploration

• Preprocessing • Data mining algorithms

• Evaluation & Interpretation

• What data do I need to answer question X?

• What variables are in the data?

• Basic stats of my data?


Data Input & Exploration

“LikeMiner”

• Cleanup!

• Choose a suitable data model

• What happens if you integrate data from multiple sources?

• Reformat your data


Preprocessing

“LikeMiner”

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

• Association: Finding relationships between variables

• Clustering: Discovering groups and structures in data


Data Mining Algorithms

• 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”

Evaluation & InterpretationWhat 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


source: http://kunau.us/wp-content/uploads/2011/02/Screen-shot-2011-02-09-

at-9.03.46-PM-w600-h900.png


Mining Social Web Data

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

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


Single Person

http://infosthetics.com/archives/2011/12/all_the_information_facebook_knows_about_you.html

http://www.youtube.com/watch?feature=player_embedded&v=kJvAUqs3Ofg

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


Populations

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


Brand Sentiment via Twitter

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

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


Sentiment Analysis as Service

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


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


Recommended Reading

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

http://www.actmedia.eu/media/img/text_zones/English/small_38421.jpgSocial 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

http://www.actmedia.eu/media/img/text_zones/English/small_38421.jpg

Image Source: http://blog.compete.com/wp-content/uploads/2012/03/Like.jpgSocial Web 2014, Lora Aroyo!

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

http://blog.compete.com/wp-content/uploads/2012/03/Like.jpg

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

Hands-on Teaser

• Build your own recommender system 101 • Recommend pages on del.icio.us • Recommend pages to your Facebook friends

http://www.flickr.com/photos/bionicteaching/1375254387/

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

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