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Mining the Social Web

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There's a short write-up on some of the story behind the 2nd Edition of Mining the Social Web on the DataScience@Berkeley blog that's a quick and fun read
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Mining the Social Web

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An advisory for consumers of Twitter's Streaming API:

https://bit.ly/1aDgmHv 

Let me know if there's anything else I can do to help in the meanwhile. 

I'm here for you and want to see you be successful in all that you're doing with your social web mining pursuits!
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Mining the Social Web

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Some lessons learned from writing 5 tech books in 5 years...
 
For most of 2013, most of my nights and weekends have been consumed with a writing (and selling) a book entitled Mining the Social Web (2nd Edition). This makes the fifth tech book that I’ve written in approximately five years, and one thing I’ve come to…
For most of 2013, most of my nights and weekends have been consumed with a writing (and selling) a book entitled Mining the Social Web (2nd Edition). This makes the fifth tech book that I've writte...
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Mining the Social Web

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All I want for Christmas are...book reviews! If you have been enjoying Mining the Social Web, would you consider writing up a short review on Amazon? I'm hoping to exceed 25 reviews by the end of the year. Only a few more to go...
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Mining the Social Web

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Mining the Social Web 2E was recently added to O'Reilly Media's Data Science Starter Kit - a collection of more than a dozen books about data science. There's a lot of good stuff in there.

However, I also recommend checking out my own handpicked collection of Essential Data Science books on Amazon. I personally own, have benefitted from all of these books, and have left brief comments to help you decide if any of them may be appropriate for your own personal enrichment.

O'Reilly Media's Data Science Starter Kit - http://bit.ly/18LmCfy

Essential Data Science Books (Amazon) - http://amzn.to/1bimqTp

If you have other essential recommendations, please leave a comment!
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Mining the Social Web

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You can now browse GitHub repositories of IPython Notebooks with the hosted notebook viewer! 

Check it out live:

http://nbviewer.ipython.org/github/ptwobrussell/Mining-the-Social-Web-2nd-Edition/tree/master/ipynb/
Name .. Chapter 0 - Preface.ipynb · Chapter 1 - Mining Twitter.ipynb · Chapter 2 - Mining Facebook.ipynb · Chapter 3 - Mining LinkedIn.ipynb · Chapter 4 - Mining Google+.ipynb · Chapter 5 - Mining Web Pages.ipynb · Chapter 6 - Mining Mailboxes.ipynb · Chapter 7 - Mining GitHub.ipynb ...
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Mining the Social Web

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Looking for a fun weekend project? Try exporting your GMail with Google Takeout and use IPython Notebook and MongoDB to do some data mining. Details here: http://wp.me/p3QiJd-5P
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Mining the Social Web

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A round up of various blog posts, presentations, excerpts, and videos about data mining with Twitter before (somewhat) markedly moving onto the myriad topics involving other social web properties and data sets.

Suggestions for future topics are welcome!
Since the release of Mining the Social Web, 2E in late October of last year, I have mostly focused on creating supplemental content that focused on Twitter data. This seemed like a natural starting...
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Mining the Social Web, 2nd Edition is currently priced at only $8.49 on Kindle for a limited time. That's 81% off, and I'm not sure that you'll ever see it priced this inexpensively ever again. 

If you prefer collecting/reading books on the Kindle (or know someone who does), this is a great opportunity to pick up a copy.


See here for more details: http://amzn.to/1evWfrp
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So, a funny thing happened on the way to the San Mateo Barnes & Noble bookstore today...

Thoughts or comments about how this book is being staged in the photo? Leave a comment! I'm curious what you think...

I'll also mail a signed copy to whoever comes up with the best caption for this photo by the end of the year. (If anyone is a Photoshop guru and wants to work some visual enhancements into play, it will likely bear strongly in the judging process...) Just leave a comment that is prefixed with "Caption" and you'll be eligible.

Happy holidays to everyone!
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Mining the Social Web

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A video archive of the mini-workshop Data Science Experiments with Twitter and IPython Notebook is now available. Enjoy!
 
The video archive just became available for my mini-workshop last week on "Data Science Experiments with Twitter and IPython Notebook"[1]. This video could potentially be useful for anyone who may be considering my (broader) PyCon 2014 tutorial on "Mining Social Web APIs with IPython Notebook"[2].

PS - As luck would have it, Mining the Social Web 2E is O'Reilly Media's Deal of the Day for today only and is 50% off until midnight PST[3]. (Use code DEAL at checkout.)

PPS Guido likes it[4], and if it's good enough for Guido...well... :)

Happy Holidays to everyone!

[1] http://oreillynet.com/pub/e/2984

[2] https://us.pycon.org/2014/schedule/presentation/134/

[3] http://bit.ly/135dHfs

[4] https://twitter.com/gvanrossum/status/404746061865967616
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This WSJ post says that "Twitter says fake accounts represent 5% of its users. Well, I guess it all depends on how you define "fake". My own research initially suggests a much higher number.
 
A must-read piece from WSJ [1] that's highly related to a piece I've been working on and just put up last week that investigates some of the same issues[2]. The piece from WSJ exposes some details surrounding a "black market" for acquiring fake Twitter followers while my piece provides some initial data mining, complete with source code and results.

(My results suggested that up to 50% of celebrity Twitter users (including Lady Gaga) are 'suspect' in the sense that they're spam-bots or abandoned accounts; more analysis is pending on a breakdown on the 'suspect' accounts with an update to follow.)

A snippet from the WSJ piece:

"Some entertainers pay for fake followers. But false accounts can be political tools as well. In 2011, thousands of fake accounts disrupted anti-Kremlin protesters on Twitter.

The fake accounts remain a cloud over Twitter Inc. in the wake of its successful initial public offering. 'Twitter is where many people get news,' says Sherry Turkle, director of the MIT Initiative on Technology and Self. 'If what is trending on Twitter is being faked by robots, people need to know that. This will and should undermine trust.'"

[1] http://online.wsj.com/news/articles/SB10001424052702304607104579212122084821400

[2] http://miningthesocialweb.com/2013/11/22/what-do-tim-oreilly-lady-gaga-and-marissa-mayer-all-have-in-common/
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Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
Introduction

How can you tap into the wealth of social web data to discover who’s making connections with whom, what they’re talking about, and where they’re located? With this expanded and thoroughly revised edition, you’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.

  • Employ IPython Notebook, the Natural Language Toolkit, NetworkX, and other scientific computing tools to mine popular social web sites
  • Apply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language data
  • Bootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projects
  • Build interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkit
  • Take advantage of more than two-dozen Twitter recipes, presented in O’Reilly’s popular "problem/solution/discussion" cookbook format

The example code for this unique data science book is maintained in a public GitHub repository. It’s designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.

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