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Social Web���2015
Lecture 4: How do we MINE, ANALYSE & VISUALISE the Social Web?
Anca Dumitrache & Lora AroyoThe Network Institute
VU University Amsterdam
• 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
Social Web 2015, Lora Aroyo
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?
Social Web 2015, Lora Aroyo
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?
Social Web 2015, Lora Aroyo
Image: http://www.co.olmsted.mn.us/prl/
propertyrecords/RecordingDocuments/PublishingImages/forms.jpg
This doesn’t work
Social Web 2015, 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 2015, Lora Aroyo
The Rise of the Data Scientist
Data Geeks Skills: !Statistics!
Data munging !Visualisation!
Social Web 2015, Lora Aroyo
http://radar.oreilly.com/2010/06/what-is-data-science.html
The Rise of the Data Scientist
Social Web 2015, Lora Aroyo
• 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
Social Web 2015, Lora Aroyo
Popular Data Products
Data Science is about building products
not just answering questionsSocial Web 2015, Lora Aroyo
Popular Data Products
empower the others to use the data
empower the others to their own analysis
Social Web 2015, Lora Aroyo
(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
Social Web 2015, Lora Aroyo
Databases Statistics
Artificial Intelligence
Data Mining 101
• Data input & exploration
• Preprocessing• Data mining algorithms
• Evaluation & Interpretation
Social Web 2015, Lora Aroyo
• 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 2015, Lora Aroyo
• Cleanup!
• Choose a suitable data model
• What happens if you integrate data from multiple sources?
• Reformat your data
Preprocessing
“LikeMiner”
Social Web 2015, Lora Aroyo
• Classification: Generalising a known structure & apply to new data
• Association: Finding relationships between variables
• Clustering: Discovering groups and structures in data
Data Mining Algorithms
Social Web 2015, Lora Aroyo
• 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”
Social Web 2015, 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 2015, Lora Aroyo
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
Social Web 2015, Lora Aroyo
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
Social Web 2015, Lora Aroyo
Brand Sentiment via Twitter
http://flowingdata.com/2011/07/25/brand-sentiment-showdown/
Social Web 2015, Lora Aroyo
http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf
Recommended Reading
Social Web 2015, Lora Aroyo
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: 3 March 23:59!
Image Source: http://blog.compete.com/wp-content/uploads/2012/03/Like.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: 12 Mar, during Hands-on• Submit final assignment : 27 March 23:59
Social Web 2015, Lora Aroyo