You are previewing Mining the Social Web.

Mining the Social Web

Cover of Mining the Social Web by Matthew A. Russell Published by O'Reilly Media, Inc.
  1. Mining the Social Web
  2. SPECIAL OFFER: Upgrade this ebook with O’Reilly
  3. Preface
    1. Content Updates
      1. February 22, 2012
    2. To Read This Book?
    3. Or Not to Read This Book?
    4. Tools and Prerequisites
    5. Conventions Used in This Book
    6. Using Code Examples
    7. Safari® Books Online
    8. How to Contact Us
    9. Acknowledgments
  4. 1. Introduction: Hacking on Twitter Data
    1. Installing Python Development Tools
    2. Collecting and Manipulating Twitter Data
      1. Tinkering with Twitter’s API
      2. Frequency Analysis and Lexical Diversity
      3. Visualizing Tweet Graphs
      4. Synthesis: Visualizing Retweets with Protovis
    3. Closing Remarks
  5. 2. Microformats: Semantic Markup and Common Sense Collide
    1. XFN and Friends
    2. Exploring Social Connections with XFN
      1. A Breadth-First Crawl of XFN Data
    3. Geocoordinates: A Common Thread for Just About Anything
      1. Wikipedia Articles + Google Maps = Road Trip?
    4. Slicing and Dicing Recipes (for the Health of It)
    5. Collecting Restaurant Reviews
    6. Summary
  6. 3. Mailboxes: Oldies but Goodies
    1. mbox: The Quick and Dirty on Unix Mailboxes
    2. mbox + CouchDB = Relaxed Email Analysis
      1. Bulk Loading Documents into CouchDB
      2. Sensible Sorting
      3. Map/Reduce-Inspired Frequency Analysis
      4. Sorting Documents by Value
      5. couchdb-lucene: Full-Text Indexing and More
    3. Threading Together Conversations
      1. Look Who’s Talking
    4. Visualizing Mail “Events” with SIMILE Timeline
    5. Analyzing Your Own Mail Data
      1. The Graph Your (Gmail) Inbox Chrome Extension
    6. Closing Remarks
  7. 4. Twitter: Friends, Followers, and Setwise Operations
    1. RESTful and OAuth-Cladded APIs
      1. No, You Can’t Have My Password
    2. A Lean, Mean Data-Collecting Machine
      1. A Very Brief Refactor Interlude
      2. Redis: A Data Structures Server
      3. Elementary Set Operations
      4. Souping Up the Machine with Basic Friend/Follower Metrics
      5. Calculating Similarity by Computing Common Friends and Followers
      6. Measuring Influence
    3. Constructing Friendship Graphs
      1. Clique Detection and Analysis
      2. The Infochimps “Strong Links” API
      3. Interactive 3D Graph Visualization
    4. Summary
  8. 5. Twitter: The Tweet, the Whole Tweet, and Nothing but the Tweet
    1. Pen : Sword :: Tweet : Machine Gun (?!?)
    2. Analyzing Tweets (One Entity at a Time)
      1. Tapping (Tim’s) Tweets
      2. Who Does Tim Retweet Most Often?
      3. What’s Tim’s Influence?
      4. How Many of Tim’s Tweets Contain Hashtags?
    3. Juxtaposing Latent Social Networks (or #JustinBieber Versus #TeaParty)
      1. What Entities Co-Occur Most Often with #JustinBieber and #TeaParty Tweets?
      2. On Average, Do #JustinBieber or #TeaParty Tweets Have More Hashtags?
      3. Which Gets Retweeted More Often: #JustinBieber or #TeaParty?
      4. How Much Overlap Exists Between the Entities of #TeaParty and #JustinBieber Tweets?
    4. Visualizing Tons of Tweets
      1. Visualizing Tweets with Tricked-Out Tag Clouds
      2. Visualizing Community Structures in Twitter Search Results
    5. Closing Remarks
  9. 6. LinkedIn: Clustering Your Professional Network for Fun (and Profit?)
    1. Motivation for Clustering
    2. Clustering Contacts by Job Title
      1. Standardizing and Counting Job Titles
      2. Common Similarity Metrics for Clustering
      3. A Greedy Approach to Clustering
      4. Hierarchical and k-Means Clustering
    3. Fetching Extended Profile Information
    4. Geographically Clustering Your Network
      1. Mapping Your Professional Network with Google Earth
      2. Mapping Your Professional Network with Dorling Cartograms
    5. Closing Remarks
  10. 7. Google+: TF-IDF, Cosine Similarity, and Collocations
    1. Harvesting Google+ Data
    2. Data Hacking with NLTK
    3. Text Mining Fundamentals
      1. A Whiz-Bang Introduction to TF-IDF
      2. Querying Google+ Data with TF-IDF
    4. Finding Similar Documents
      1. The Theory Behind Vector Space Models and Cosine Similarity
      2. Clustering Posts with Cosine Similarity
      3. Visualizing Similarity with Graph Visualizations
    5. Bigram Analysis
      1. How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions
    6. Tapping into Your Gmail
      1. Accessing Gmail with OAuth
      2. Fetching and Parsing Email Messages
    7. Before You Go Off and Try to Build a Search Engine…
    8. Closing Remarks
  11. 8. Blogs et al.: Natural Language Processing (and Beyond)
    1. NLP: A Pareto-Like Introduction
      1. Syntax and Semantics
      2. A Brief Thought Exercise
    2. A Typical NLP Pipeline with NLTK
    3. Sentence Detection in Blogs with NLTK
    4. Summarizing Documents
      1. Analysis of Luhn’s Summarization Algorithm
    5. Entity-Centric Analysis: A Deeper Understanding of the Data
      1. Quality of Analytics
    6. Closing Remarks
  12. 9. Facebook: The All-in-One Wonder
    1. Tapping into Your Social Network Data
      1. From Zero to Access Token in Under 10 Minutes
      2. Facebook’s Query APIs
    2. Visualizing Facebook Data
      1. Visualizing Your Entire Social Network
      2. Visualizing Mutual Friendships Within Groups
      3. Where Have My Friends All Gone? (A Data-Driven Game)
      4. Visualizing Wall Data As a (Rotating) Tag Cloud
    3. Closing Remarks
  13. 10. The Semantic Web: A Cocktail Discussion
    1. An Evolutionary Revolution?
    2. Man Cannot Live on Facts Alone
      1. Open-World Versus Closed-World Assumptions
      2. Inferencing About an Open World with FuXi
    3. Hope
  14. Index
  15. About the Author
  16. Colophon
  17. SPECIAL OFFER: Upgrade this ebook with O’Reilly
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Geographically Clustering Your Network

With the know-how to access extended LinkedIn profile information, and a working knowledge of common clustering algorithms, all that’s left is to introduce a nice visualization that puts it all together. The next section applies k-means to the problem of clustering your professional contacts and plots them out in Google Earth. The section after it introduces an alternative visualization called a Dorling Cartogram , which is essentially a geographically clustered bubble chart that lets you easily visualize how many of your contacts live in each state. Ironically, it doesn’t explicitly use a clustering algorithm like k-means at all, but it still produces intuitive results that are mapped out in 2D space, and it conveys a semblance of geographic clustering and frequency information.

Mapping Your Professional Network with Google Earth

An interesting exercise in seeing k-means in action is to use it to visualize and cluster your professional LinkedIn network by putting it on a map—or the globe, if you’re a fan of Google Earth. In addition to the insight gained by visualizing how your contacts are spread out, you can analyze clusters by using your contacts, the distinct employers of your contacts, or the distinct metro areas in which your contacts reside as a basis. All three approaches might yield results that are useful for different purposes. Through the LinkedIn API, you can fetch location information that describes the major metropolitan area, ...

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