Apr 012011
 

Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, by Matthew A. Russell

Just released by O’Reilly:

Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who’s making connections with social media, what they’re talking about, or where they’re located? This concise and practical book, Mining the Social Web (O’Reilly Media, $39.99 USD), shows you how to answer these questions and more. You’ll learn how to combine social web data, analysis techniques, and visualization to help you find what you’ve been looking for in the social haystack, as well as useful information you didn’t know existed.


“Data is the new black,” says author Matthew Russell (@SocialWebMining). “Social networking sites aren’t just fads anymore. It’s clear that they’re here to stay, and the mining and analysis space on these treasure troves of data is extremely nascent. I really believe that the next one to three years will offer especially novel and rewarding opportunities for anyone who is willing to think carefully about the space and put in the sweat equity to work on the right problems.”

With his latest book, Russell says that, “Readers will be empowered to think critically about the social data space and put their analysis and data mining ideas into practice by adapting examples from the book to solve problems of value.”

Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.

  • Get a straightforward synopsis of the social web landscape
  • Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn
  • Learn how to employ easy-to-use Python tools to slice and dice the data you collect
  • Explore social connections in microformats with the XHTML Friends Network
  • Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
  • Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits

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