Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

by Matthew Russell, Mikhail Klassen

Overview

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers.

In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter.

• Get a straightforward synopsis of the social web landscape
• Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook
• Adapt and contribute to the code’s open source GitHub repository
• Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect
• Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition
• Build beautiful data visualizations with Python and JavaScript toolkits

Product Details

ISBN-13:	9781491985045
Publisher:	O'Reilly Media, Incorporated
Publication date:	01/17/2019
Pages:	426
Product dimensions:	6.90(w) x 9.10(h) x 1.00(d)

About the Author

Matthew Russell (@ptwobrussell) is Chief Technology Officer at Built Technologies, where he leads a team of leaders on a mission to improve the way the world is built. Outside of work, he contemplates ultimate reality, practices rugged individualism, and trains for the possibilities of a zombie or robot apocalypse.

Mikhail Klassen is Chief Data Scientist at Paladin AI, a startup creating adaptive training technologies. He has a PhD in computational astrophysics from McMaster Universityand a BS in applied physics from Columbia University. Mikhail is passionate about artificial intelligence and how the tools of data science can be used for good. When not working at a startup, he's usually reading or traveling.

Table of Contents

• Preface
  
• A Guided Tour of the Social Web
  
  • Prelude
    
  • 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
    
  • 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
    
  
  
  
  
• Twitter Cookbook
  
  • Chapter 9: Twitter Cookbook
    
  
  
  
  
• Appendixes
  
  • Information About This Book's Virtual Machine Experience
    
  • OAuth Primer
    
  • Python and IPython Notebook Tips & Tricks
    
  
  
  
  
• Colophon
  

Editorial Reviews

Mining the social web, again

When we first published Mining the Social Web, I thought it was one of the most important books I worked on that year. Now that we’re publishing a second edition (which I didn’t work on), I find that I agree with myself. With this new edition, Mining the Social Web is more important than ever.
 
While we’re seeing more and more cynicism about the value of data, and particularly “big data,” that cynicism isn’t shared by most people who actually work with data. Data has undoubtedly been overhyped and oversold, but the best way to arm yourself against the hype machine is to start working with data yourself, to find out what you can and can’t learn. And there’s no shortage of data around. Everything we do leaves a cloud of data behind it: Twitter, Facebook, Google+ — to say nothing of the thousands of other social sites out there, such as Pinterest, Yelp, Foursquare, you name it. Google is doing a great job of mining your data for value. Why shouldn’t you?

There are few better ways to learn about mining social data than by starting with Twitter; Twitter is really a ready-made laboratory for the new data scientist. And this book is without a doubt the best and most thorough approach to mining Twitter data out there. But that’s only a starting point. We hear a lot in the press about sentiment analysis and mining unstructured text data; this book shows you how to do it. If you need to mine the data in web pages or email archives, this book shows you how. And if you want to understand how to people collaborate on projects, Mining the Social Web is the only place I’ve seen that analyzes GitHub data.

All of the examples in the book are available on Github. In addition to the example code, which is bundled into IPython notebooks, Matthew has provided a VirtualBox VM that installs Python, all the libraries you need to run the examples, the examples themselves, and an IPython server. Checking out the examples is as simple as installing Virtual Box, installing Vagrant, cloning the 2nd edition’s Github archive, and typing “vagrant up.”  You can execute the examples for yourself in the virtual machine; modify them; and use the virtual machine for your own projects, since it’s a fully functional Linux system with Python, Java, MongoDB, and other necessities pre-installed. You can view this as a book with accompanying examples in a particularly nice package, or you can view the book as “premium support” for an open source project that consists of the examples and the VM.
 
If you want to engage with the data that’s surrounding you, Mining the Social Web is the best place to start. Use it to learn, to experiment, and to build your own data projects.
 
— Mike Loukides
Vice President of Content Strategy for O'Reilly Media, Inc.

From the Publisher