Social WebLecture 4

How can we MINE, ANALYSE and VISUALISE the Social Web? (1)

Marieke van ErpThe Network Institute

VU University Amsterdam

Why?

• UCG provides an enormous wealth of data

• insights in users’ daily lives

• insights in communities

• insights in trends

To whom it may concern

• Politicians

• Companies

• Governmental institutions

• You?

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

Questions to Ask

• Who uploads/talks? (age, gender, nationality, community)

• What are the trending topics?

• What else do these users like?

• Who are the most/least active users?

• etc.

What do you prefer?

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

The Rise of the Data Scientist

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

The Rise of the Data Scientist

• 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.

Popular Data Products

Data Mining 101

(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 and analysis of large quantities ofdata in order to discover valid, novel, potentially useful, andultimately understandable patterns in data.

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

Data Mining 101

Databases Statistics

Artificial Intelligence

Steps

• 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?

Input & Exploration in ‘LikeMiner’

Preprocessing

• Cleanup!

• Choose a suitable data model

• What happens if you integrate data from multiple sources?

• Reformat your data

Preprocessing in ‘LikeMiner’

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

Interpreting your results

Data Mining is not easy

Mining Social Web Data

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

at-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/

Recommended Reading

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

Final Assignment: Your SocWeb App

• Create a Social Web app with your group

• Use structured data, relationships between entities, data analysis, visualisation

• Write individual research report on one of the main aspects of your app

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