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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More Paperback

ISBN-13: 978-1449367619 ISBN-10: 1449367615 Edition: Second Edition

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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More + Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython + Doing Data Science: Straight Talk from the Frontline
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Product Details

  • Paperback: 448 pages
  • Publisher: O'Reilly Media; Second Edition edition (October 20, 2013)
  • Language: English
  • ISBN-10: 1449367615
  • ISBN-13: 978-1449367619
  • Product Dimensions: 9.2 x 7.1 x 0.9 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.8 out of 5 stars  See all reviews (40 customer reviews)
  • Amazon Best Sellers Rank: #23,262 in Books (See Top 100 in Books)

Editorial Reviews

Review

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.

Book Description

Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

Customer Reviews

4.8 out of 5 stars
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This book is extremely practical and has great code samples.
Greg
Additionally it gives you the tools and the code for querying social media APIs and analyzing your data in a meaningful way.
Bernard Enjolras
A great read and resource definitely worth having on your bookshelf.
Vince DiMascio

Most Helpful Customer Reviews

14 of 14 people found the following review helpful By Bernard Enjolras on October 10, 2013
Format: Paperback
The second edition of Mining the Social Web is not just an update of the previous edition (including Google+, GitHub, and Twitter API 1.1) but a new book. The book has been rethought in its entirety with a focus on pedagogy and practical use of the code. With the help of a virtual machine and IPython notebook (both made available by the author) it is possible to run the code without difficulty. The book includes a Twitter Cookbook section which is very useful if you want to mine Twitter. In my opinion this book is the best introduction to real-world programming in Python. It introduces many concepts and tools related to modern web-programming and data-mining. Additionally it gives you the tools and the code for querying social media APIs and analyzing your data in a meaningful way. Matthew Russell has realized a tour de force with the new edition of this book: introducing advanced programming concepts and tools in a pedagogic, accessible and practical way.
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7 of 7 people found the following review helpful By Greg on November 4, 2013
Format: Paperback
This book is extremely practical and has great code samples. It's easy to follow and fun! If you're interested in mining Twitter data, there is an (large) chapter focused entirely on reproducible code snippets that use the Twitter API.
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9 of 10 people found the following review helpful By NSlone on November 3, 2013
Format: Kindle Edition
Why in the world would anyone want to mine data from social websites, you may be asking yourself just about now. Good question. Suppose you were in the process of creating a product, but at the same time you are curious as to which niche it would fit into. You may also be curious as to which niche is the most financially beneficial for your product, as well as perhaps you should tweak it to maximize your particular niche after mining the web for this data.

Who would benefit from this product the most? And best of all, which social websites do your prospective buyers frequent the most. Is it Facebook? What about Twitter? Do they have a membership on LinkedIn? Are they a member of Google+? Regardless of where they may be, there is a good chance that your data mining will pay off.

There is plenty of example code, which makes use of the Python language. There is also IPython Notebook which is an interactive Python interpreter which gives you a notebook like experience from your web browser. With a few clicks from within IPython Notebook, you can be well on your way to learning more about the users of social websites than you might have ever thought possible.

A part of the paragraph on IPython is paraphrased from the books itself. I would definitely recommend this book to others. It looks great on my Kindle Fire HD.
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5 of 5 people found the following review helpful By Mark Meanwell on November 3, 2013
Format: Paperback
Great guidebook to acquiring and analyzing data from leading social media sites, including Twittter, Facebook, Google +, LinkedIn and GitHub along with other web tips and tricks. The iPython notebook approach provides turn key like method to run examples and check results in line, which accelerates and reinforces the topics.

Whether you are new to social media API's and want a straightforward way to ramp up learning and discovery of social mining techniques or more seasoned user, this book has it covered. Chapter formats and exercises make it easy to work a variety of topics and are laid out in easy to follow and execute fashion.

Highly recommend, so get the book and get started!
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5 of 5 people found the following review helpful By chris725 on October 12, 2013
Format: Paperback
Mining the Social Web v2 is remarkable in terms of its simplicity as well as its depth. The author has focused on reducing friction to learning and executing traditionally difficult topics such as text mining and natural language processing. I already own the first version of MtSW, and between the new topics (LinkedIn, GitHub, Google+) and the new infrastructure (IPython, VirtualBox, etc) this is like a whole new book full of inspiration and ideas. The fact that a lot of this book is a significantly different than the first edition isn't surprising since the topic of the social web is evolving so rapidly.

The reason this is such an important book is that it teaches non-experts to build simple systems for making decisions on data that is constantly up-to-date. It's an end-to-end manual for continuously gathering data (e.g. Twitter API), analyzing data (e.g. Natural Language Processing), and presenting information (e.g. D3). By significantly reducing the barrier to building these systems, Matthew has increased the number of people on the planet that can provide data for making proper decisions . . . and data always beats opinions.

This is one of the rare books that does a great job of introducing deep technical topics AND providing an easy, practical implementation. Unlike a lot of tech books, MtSW makes it trivial to get started through a combination of Vagrant, VirtualBox, IPython Notebook, and GitHub such that you can have all the updated examples up and running within minutes. I'm much more of a practitioner (read: Hacker) than a computer scientist so this is exactly the right amount of technical detail to try out an idea.
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4 of 4 people found the following review helpful By Carsten Jřrgensen on January 22, 2014
Format: Paperback
Book review - Mining the Social Web, 2nd Edition by Matthew A. Russell, O'Reilly Media

Introduction
Last year I read an article in Nature about Paul Erdős’s on the occasion of his 100th birthday. Outside mathematical circles Erdős is most known for the so called Erdős number. There are several different definitions of the Erdős number but according to Wikipedia it is defines as the "'collaborative distance' between a person and mathematician Paul Erdős, as measured by authorship of mathematical papers". So if you co-authored a paper with Erdős your Erdős number is 1. Your number will be 2 if you co-authored a paper with an author who wrote a paper directly with Erdős and so forth. Analyzing Erdős numbers is an application of social network theory and ever since I read the article I wanted to learn more about data mining applied to modern social media platforms. When researching for a book on this topic I came across Mining the Social Web and the books very practical approach convinced me to that this was the book I wanted to read.

Virtual Machine experience
The book is accompanied with a Virtual Machine experience that sets new standards for interactions between technical programming books and the code samples provided by the book. In no time you are up and running with the code samples in a IPython notebook that also can be edited and used as basis for your own data mining experiments. I would really love to see this approach adopted by other programming books.
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