Slides from Matthew Russell's workshop on Mining Social Web APIs with IPython Notebook at Data Day Texas 2015 (Austin, TX).

1. Mining Social Web APIs
with IPython Notebook
Matthew A. Russell - @ptwobrussell - http://MiningTheSocialWeb.com
Austin - 9 January 2015
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2. Intro
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3. Hello, My Name Is ... Matthew
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Background in Computer Science
Data mining & machine learning
CTO @ Digital Reasoning Systems
Data mining; machine learning
Author @ O'Reilly Media
5 published books on technology
Principal @ Zaffra
Selective boutique consulting

4. Transforming Curiosity Into Insight
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An open source software (OSS) project
http://bit.ly/MiningTheSocialWeb2E
A book
http://bit.ly/135dHfs
Accessible to (virtually) everyone
Virtual machine with turn-key coding
templates for data science experiments
Think of the book as "premium" support for the
OSS project

5. Table of Contents (1/2)
Chapter 1 - Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking
About, and More
Chapter 2 - Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More
Chapter 3 - Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More
Chapter 4 - Mining Google+: Computing Document Similarity, Extracting Collocations, and
More
Chapter 5 - Mining Web Pages: Using Natural Language Processing to Understand Human
Language, Summarize Blog Posts, and More
Chapter 6 - Mining Mailboxes: Analyzing Who's Talking to Whom About What, How Often, and
More
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6. Table of Contents (2/2)
Chapter 7 - Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs,
and More
Chapter 8 - Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing
over RDF, and More
Chapter 9 - Twitter Cookbook
Appendix A - Information About This Machine's Virtual Machine Experience
Appendix B - OAuth Primer
Appendix C - Python and IPython Notebook Tips & Tricks
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7. Designed for Pedagogy
Brief Intro
Objectives
API Primer
Analysis Technique(s)
Data Visualization
Recap
Suggested Exercises
Recommended Resources
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8. The Social Web Is All the Rage
World population: ~7B people
Facebook: 1.15B users
Twitter: 500M users
Google+ 343M users
LinkedIn: 238M users
~200M+ blogs (conservative estimate)
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*Estimates as of early 2014

9. Overview
Module 1 - Virtual Machine Setup (30 mins)
Module 2 - Mining Twitter (90 mins)
BREAK (15 mins)
Module 3 - Mining Facebook (60 mins)
Lunch (75 mins)
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Module 4 - Mining LinkedIn (75 mins)
BREAK (15 mins)
Module 5 - Open/Choice (75 mins)
Module 6 - Privacy & Ethics; (15 mins)
Module 7 - Final Q&A (15 mins)

10. Module Format
~10-15 minutes of exposition
I talk; you listen
~15 minutes of independent (or collaborative) work
You hack while I walk around and help you
~5 minutes of recap with Q&A
You ask; I try to answer
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11. Workshop Objective
To send you away as a social web hacker
Broad working knowledge popular social web APIs
Hands-on experience hacking on social web data with a common toolkit
Not for me talk to you for 8 straight hours
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12. Just a Few More Things
This workshop is...
An adaptation of Mining the Social Web, 2nd Edition
More of a guided hacking session where you follow along (vs a preso)
Wider than it is deeper
There's only so much you can do in a few hours
I'm available 24/7 this week (and beyond) to help you be successful
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13. Assumptions
At some point in your life, you have
Programmed with Python
Worked with JSON
Made requests and processed responses to/from web servers
Or you want to learn to do these things now...
And you're a quick learner
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14. Module 1: Virtual Machine Setup
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15. Why do you need a VM?
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To save yourself a lot of time
Because installation and configuration management is tedious and time-
consuming
So that you can focus on the task at hand instead
So that I can support you regardless of your hardware and operating
system

16. But I can do all of that myself...
True...
If you would rather troubleshoot unexpected installation/configuration issues
instead of immediately focusing on the real task at hand
At least give it a shot before resorting to your own devices so that you
don't have to install specific versions of ~40 Python packages
Including scientific computing tools that require underlying C/C++ code to
be compiled
Which requires specific versions of developer libraries to be installed
You get the idea...
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17. The Virtual Machine Experience
Vagrant
A nice abstraction around virtual machine providers
One ring to rule them all
Virtualbox, VMWare, AWS, ...
IPython Notebook
The easiest way to program with Python
A better REPL (interpreter)
Great for hacking
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18. What happens when you vagrant up?
Vagrant follows the instructions in your Vagrantfile
Starts up a Virtualbox instance
Uses Chef to provision it
Installs OS patches/updates
Installs MTSW software dependencies
Starts IPython Notebook server on port 8888
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19. Why Should I Use IPython Notebook?
Because it's great for hacking
And hacking is usually the first step
Because it's great for collaboration
Sharing/publishing results is trivial
Because the UX is as easy as working in a notepad
Think of it as "executable paper"
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20. 20

21. 21

22. VM Quick Start Instructions
Go to http://MiningTheSocialWeb.com/quick-start/
Follow the instructions
And watch the screencasts!
Basically:
Install Virtualbox & Vagrant
Run "vagrant up" in a terminal to start a guest VM
Then, go to http://localhost:8888 on your host machine's web browser
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23. What Could Be Easier?
A hosted version of the VM!
But only for a few hours during this workshop
Because it costs money to run these servers
Go to http://bit.ly/XXX and pick a machine
Do not share the URLs outside of this workshop!
Please don't try to hack the machines
Learn how I arrived at this setup at http://MiningTheSocialWeb.com
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24. Module 2: Mining Twitter
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25. Objectives
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Be able to identify Twitter primitives
Understand tweet metadata and how to use it
Learn how to extract entities such as user mentions, hashtags, and URLs
from tweets
Apply techniques for performing frequency analysis with Python
Be able to plot histograms of Twitter data with IPython Notebook

26. Twitter Primitives
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Accounts Types: "Anything"
"Following" Relationships
Favorites
Retweets
Replies
(Almost) No Privacy Controls

27. API Requests
RESTful requests
Everything is a "resource"
You GET, PUT, POST, and DELETE resources
Standard HTTP "verbs"
Example: GET https://api.twitter.com/1.1/statuses/user_timeline.json?
screen_name=SocialWebMining
Streaming API filters
JSON responses
Cursors (not quite pagination)
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28. Twitter is an Interest Graph
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Roberto Mercedes
Jorge
Ana
Nina
Johnny
Araya
Rodolfo
Hernández

29. What's in a Tweet?
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140 Characters ...
... Plus ~5KB of metadata!
Authorship
Time & location
Tweet "entities"
Replying, retweeting, favoriting, etc.

30. What are Tweet Entities?
Essentially, the "easy to get at" data in the 140 characters
@usermentions
#hashtags
URLs
multiple variations
(financial) symbols
stock tickers
media
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31. Data Mining = Curiosity + Stats
Curiosity
Interests, desires, and intuitions
Statistics
Counting
Comparing
Filtering
Ranking
Hypothesis testing; knowledge discovery
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32. Histograms
A chart that is handy for frequency analysis
They look like bar charts...except they're not bar charts
Each value on the x-axis is a range (or "bin") of values
Not categorical data
Each value on the y-axis is the combined frequency of values in each range
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33. 33
Example: Histogram of Retweets

34. Social Media Analysis Framework
A memorable four step process to guide data science experiments:
Aspire
To test a hypothesis (answer a question)
Acquire
Get the data
Analyze
Count things
Summarize
Plot the results
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35. Exercises
Review Python idioms in the "Appendix C (Python Tips & Tricks)" notebook
Follow the setup instructions in the "Chapter 1 (Mining Twitter)" notebook
Fill in Example 1-1 with credentials and begin work
Execute each example sequentially
Customize queries
Explore tweet metadata; count tweet entities; plot histograms of results
Explore the "Chapter 9 (Twitter Cookbook)" notebook
Think of it as a collection of building blocks
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36. Module 3: Mining Facebook
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37. Objectives
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Be able to identify Facebook primitives
Learn about Facebook’s Social Graph API and how to make API requests
Understand how Open Graph protocol extends Facebook's Social Graph
API
Be able to analyze likes from Facebook pages and friends
The Graph v2 API changes at substantially tamped down privacy and
permissions
See https://developers.facebook.com/docs/apps/changelog

38. Facebook Primitives
Account Types: People & Pages
Mutual Connections
Likes
Shares
Comments
Extensive Privacy Controls
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39. Facebook is an Interest Graph
39
Roberto Mercedes
Jorge
Ana
Nina
Johnny
Araya
Rodolfo
Hernández

40. Graph API
Nodes
Things
Edges
Connections between things
Fields
Info (properties) about things
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41. Facebook API Explorer
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Go to https://developers.facebook.com/tools/explorer
Really, go there right now...

42. Graph API Explorer
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43. Example Graph API Requests
Social Graph API requests
Easy to learn and use
http://graph.facebook.com/me/feed
http://graph.facebook.com/me/likes
http://graph.facebook.com/me/?fields=id,name,friends.fields(likes.limit(10))
http://graph.facebook.com/Mining-the-Social-Web?fields=id,name,about,likes
JSON responses
Traditional pagination
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44. 44
Retrieve Your Likes

45. Permissions Prior to Graph v2
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46. Permissions as of Graph v2
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47. Explore Facebook Pages
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Names of pages
MiningTheSocialWeb
CrossFit
OReilly
Web URLs (OGP extensions to Facebook's Social Graph)
http://www.imdb.com/title/tt0117500

48. Social Media Analysis Framework
Recall the same four step process to guide data science experiments:
Aspire
Acquire
Analyze
Summarize
48

49. Exercises
Copy/paste your access token from the Graph API Explorer into the "Chapter 2
(Mining Facebook)" notebook
Execute and tinker with Examples 2-1 thru 2-6
Inspect content in your feed
Juxtapose public figures
Compare/contrast similar products/brands of interest
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50. Module 4: Mining LinkedIn
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51. Objectives
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Learn about LinkedIn’s Developer Platform
Understand how clustering works
A fundamental type of machine learning
Be able to employ geocoding services to arrive at a set of coordinates
from a textual reference to a location
Visualize geographic data with cartograms

52. LinkedIn Primitives
Account Types: People, Groups, Companies, Jobs
And Activity Streams
Data is typically perceived as being more sensitive
Richest data source? (Think: LinkedIn's business model)
Profile descriptions from mutual connections
A little messier than it first appears
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53. API Requests
HTTP-based Requests
Field selector syntax
http://api.linkedin.com/v1/people/~:(first-name,last-name,headline,picture-url)
XML responses
CSV address book download
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54. Is LinkedIn an Interest Graph?
Fundamentally: yes. the developer API requires you to do a bit of work to model it
Less trivial to find some of the "pivots"
e.g. There's no public Skills API for developers
But the data is there (mostly in profile descriptions) for your direct connections
Companies, job titles, job descriptions
Lots of richness is tucked away in human language data
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55. Clustering
An unsupervised machine learning learning technique
Think: an algorithm that organizes the data into partitions
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56. Example: Clustered Job Titles
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57. 3 Steps to Clustering Your Data
Normalization
Compare (similarity/distance measurement)
n-grams, edit distance, and Jaccard are common, but your imagination is the limit
Why can't you just compare everything to everything?
Dimensionality Reduction
Ideally, your clustering algorithm will mitigate the pain
k-means is among the most common clustering techniques in use
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58. Jaccard Similarity
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59. k-Means Explained
1. Randomly pick k points in the data space as initial values that will be used to
compute the k clusters: K1, K2, ..., Kk.
2. Assign each of the n points to a cluster by finding the nearest Kn—effectively
creating k clusters and requiring k*n comparisons.
3. For each of the k clusters, calculate the centroid of the cluster and reassign its Ki
value to be that value. (Hence, you’re computing “k-means” during each iteration of
the algorithm.)
4. Repeat steps 2–3 until the members of the clusters do not change between
iterations. Generally speaking, relatively few iterations are required for convergence.
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60. k-Means: Initialize
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61. k-Means: Step 1
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62. k-Means: Step 2
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63. k-Means: Step 3
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64. k-Means: (Fast-Forward) Step 9
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65. Geocoding
Transforming a location to a set of coordinates
Nashville, TN => (36.16783905029297, -86.77816009521484)
A harder problem than it first appears
The Bing API is especially generous
Requires an account sign up: http://bingmapsportal.com
Use the API key with the geopy package
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66. Introducing: The Dorling Cartogram
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67. Social Media Analysis Framework
Remember: Use the same four step process to guide data science experiments:
Aspire
Acquire
Analyze
Summarize
67

68. Exercises
Follow the instructions in the "Chapter 3 (Mining LinkedIn)" notebook to create an API
connection and follow along with the first few examples
Download your connections as a CSV file from http://www.linkedin.com/people/
export-settings and save them to your VM
A deviation from instructions in Example 3-6 is necessary for remote VMs
See http://bit.ly/mtsw-ch03-helper-code
Try clustering your contacts in Example 3-12
Use the python-linkedin client to tap into Activity Streams
See https://developer.linkedin.com/documents/get-network-updates-and-
statistics-api
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69. Module 5: Choice
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70. Objectives
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To work on "loose ends" or areas of interest from previous modules
To hack on code in notebooks not yet encountered
To setup the virtual machine on your own box if you haven't yet
To collaborate/talk and otherwise make the most of our togetherness

71. Social Media Analysis Framework
Remember:
Aspire
Acquire
Analyze
Summarize
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72. Recommendations
Setup your own development environment if you haven't already
Appendix A
Text Mining & Natural Language Processing
Chapter 4 (Mining Google+) & Chapter 5 (Mining Web Pages)
Graph Mining
Chapter 7 (Mining GitHub)
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73. Module 6: Privacy & Ethics
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74. 74
Know thy data, and know thyself
--Matthew A. Russell

75. 75
If we have data, let’s look at data.
If we have opinions, let’s go with mine
--Jim Barksdale

76. 76
In God we trust. All others must bring data
--W. Edwards Deming

77. Communication => Data
Communication
Senders
humans & machines
Messages
natural language, images, videos, etc.
Recipients
humans & machines
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78. Data Alchemy
Data: Documents & document fragments (text messages, etc.)
Information: "Assertions", summaries, tags, etc.
Knowledge: Aggregated, queryable information
Wisdom: “Compressed” knowledge
Gold: Money
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79. Machine Learning
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A program that learns (improves) from experience (data) according
to some objective
Supervised learning
Unsupervised learning
Reinforcement learning
How to do it
Program mathematical models and hope for the best...
How to do it well
Program state-of-the-art mathematical models with sufficient
representative data

80. 80
Knowledge is a process of piling up facts;
wisdom lies in their simplification
--Martin Fischer

81. 81
Any sufficiently advanced technology is
indistinguishable from magic
--Arthur C. Clarke

82. Is Privacy Already an Illusion?
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Digital happenings circa 2014
The Cloud
Social Media
Deep Learning
The Internet of Things
Internet.org

83. 83
Civilization is the progress toward a society of privacy...
-- Ayn Rand

84. 84
If you have something that you don’t want anyone to know,
maybe you shouldn’t be doing it in the first place.
-- Eric Schmidt, (former) CEO of Google

85. Influences on Ethics
Capitalism, economics, & marketing
A for-profit corporation's fiduciary duty: To maximize the common stock's value
How to do it? By transacting commerce
How do it well? By advertising more effectively than competitors
How to do it really well? With highly relevant personalized ads (recommenders)
Terms of Service (ToS) - The legal extent of ethical obligations?
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86. Module 7: Final Q&A
86

87. Free Stuff
http://MiningTheSocialWeb.com
Mining the Social Web 2E Chapter 1 (Chimera)
http://bit.ly/13XgNWR
Source Code (GitHub)
http://bit.ly/MiningTheSocialWeb2E
http://bit.ly/1fVf5ej (numbered examples)
Screencasts (Vimeo)
http://bit.ly/mtsw2e-screencasts
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