A presentation to the FrontRange PyData Meetup on how to get started with Mining the Social Web.

1. Mining the Social Web
for Fun and Profit:
A Getting Started Guide
Matthew A. Russell - @ptwobrussell - http://MiningTheSocialWeb.com
Front Range PyData Meetup - 21 May 2014
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2. Overview
Intro (5 mins)
Virtual Machine Experience (10 mins)
Virtual Machine and IPython Notebook Demonstration (10 mins)
Mining Twitter: A Primer (20 mins)
Wrap Up/Final Q&A (10 mins)
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3. Intro
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4. 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

5. Transforming Curiosity Into Insight
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An open source software (OSS) project
http://bit.ly/MiningTheSocialWeb2E
A (rewritten) 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

6. 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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7. 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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8. 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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9. Anatomy of Each Chapter
Brief Intro
Objectives
API Primer
Analysis Technique(s)
Data Visualization
Recap
Suggested Exercises
Recommended Resources
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10. The Virtual Machine Experience
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11. Why do you need a VM?
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To save time
Because installation and configuration management is harder than it first
appears
So that you can focus on the task at hand instead
So that I can support you regardless of your hardware and operating
system
Arguably, it's even a best practice for a dev environment

12. 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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13. 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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14. 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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15. 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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18. 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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19. An (AWS) Hosted Virtual Machine
Is it free?
Perhaps...
...Sign-up for the AWS free tier at http://aws.amazon.com/free/
But not right now. Do it later
See this blog post for some inspiration on how to easily build your own
AMI from Vagrant boxes
http://wp.me/p3QiJd-3T
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20. Virtual Machine and IPython
Notebook Demonstration
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21. Demonstration of Virtual Machine
http://nbviewer.ipython.org
http://MiningTheSocialWeb.com/quick-start/
Your first "vagrant up"
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22. Mining Twitter: A Primer
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23. 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

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

25. 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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26. Twitter is an Interest Graph
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Roberto Mercedes
Jorge
Ana
Nina
Johnny
Araya
Rodolfo
Hernández

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

28. 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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29. Data Mining Is...
Counting
Comparing
Filtering
Ranking
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30. 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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Example: Histogram of Retweets

32. 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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33. Recommended 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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34. Final Q&A; Wrap Up
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35. Recommended Resources
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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