VU University Amsterdam - The Social Web 2016 - Lecture 4

Social Web2016

Lecture 4: How do we MINE, ANALYSE & VISUALISE the Social Web?

Davide Ceolin (credits to: Lora Aroyo)The Network Institute

VU University Amsterdam

Announcements• Results of Assignment 1 are out: well

done• Assignment 2 is out: due on 01/03!• Next deadlines:• Wednesday 23:59: final project

update• Friday 10:00: post your question• Friday 17:00: vote your question

Announcements• This Thursday (lab session):• F153 – S345 – S329• Anca & Niels will be there in

person• I will join via hangout

([email protected])• Next Monday: guest lecture

• 200 billion tweets on Twitter in 2015, by 1.3 billion registered users

• 4.5 billion likes generated on Facebook in 2015, by 1.55 billion different users

• 300 hours of videos uploaded to YouTube every minute

• 60.7 million photos uploaded to flickr per month

The Age of BIG Data

Social Web 2016, Davide Ceolin

Science with BIG Data


BIG Data Challenges


Big Data vs. Deep Data

• Social Web data often follow a long tail distribution


Big Deep

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?


Web Source Criticism?Source criticism checklist (https://en.wikipedia.org/wiki/Source_criticism) • Who is the author and what are the qualifications of

the author in regard to the topic that is discussed?• When was the information published?• What is the reputation of the publisher?• Does the source show a particular cultural or

political bias?• Does the source contain a bibliography?• Has the material been reviewed by a group of peers,

or has it been edited?• …

How does this apply to Web sources?

https://en.wikipedia.org/wiki/Source_criticism



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?


Web of Trust

https://www.mywot.com/en/scorecard/pulse.seattlechildrens.org

Who uses it?


PoliticiansGovernmental

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


CultureHistory


CultureHistory


Culture

Bill Howe, University of Washington


Entertainment


You?


https://klout.com/#/measure

Companies


Who does it?


The Rise of the Data Scientist

Data Geeks Skills:Statistics & Math

Data mungingVisualisation


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

The Rise of the Data Scientist


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


Data Science Venn Diagram



Popular Data Products

Data Science is about building products

not just answering questionsSocial Web 2016, Davide Ceolin


empower the others to use the

data

empower the others to their own analysis



http://www.metacog.com/resources/banner3.jpg

(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


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

Databases

Statistics/ Numerical methods

Artificial Intelligen

ce

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”Social Web 2016, Davide Ceolin

• 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


Popular ML – Deep learning

http://www.kdnuggets.com/wp-content/uploads/deep-

learning-small-big-data.jpg

http://scyfer.nl/wp-content/uploads/2014/05/

Deep_Neural_Network.png

Deep learning frameworks

https://code.facebook.com/posts/1687861518126048/facebook-to-open-

source-ai-hardware-design/

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.htmlSee 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://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

Social Web 2016, Davide Ceolinhttp://www.crowdflower.com/type-sentiment-analysis

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


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

Recommended Reading




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: 1 March 23:59

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

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

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/

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

Education

VU University Amsterdam - The Social Web 2016 - Lecture 4