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Mining the Social Web

Book Description

Popular social networks such as Facebook and Twitter generate a tremendous amount of valuable data on topics and use patterns. Who's talking to whom? What are they talking about? How often are they talking? This concise and practical book shows you how to answer these questions and more by harvesting and analyzing data using social web APIs, Python, and pragmatic storage technologies such as Redis, CouchDB, and NetworkX.

With Mining the Social Web, intermediate to advanced programmers will learn how to harvest and analyze social data in way that lends itself to hacking as well as more industrial-strength analysis. Algorithms are designed with robustness and efficiency in mind so that the approaches scale well on an ordinary piece of commodity hardware. The book is highly readable from cover to cover as content progressively grows in complexity, but also lends itself to being read in an ad-hoc fashion.

  • Use easily adaptable scripts to access popular social network APIs including Twitter, OpenSocial, and Facebook

  • Learn approaches for slicing and dicing social data that's been harvested from social web APIs as well as other common formats such as email and markup formats

  • Harvest data from other sources such as Freebase and other sites to enrich your analytic capabilities with additional context

  • Visualize and analyze data in interactive ways with tools built upon rich UI JavaScript toolkits

  • Get a concise and straightforward synopsis of some practical technologies from the semantic web landscape that you can incorporate into your analysis

This book is still in progress, but you can get going on this technology through our Rough Cuts edition, which lets you read the manuscript as it's being written, either online or via PDF.

Table of Contents

  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
        1. What are people talking about right now?
        2. Extracting relationships from the tweets
      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
        1. Brief analysis of breadth-first techniques
    3. Geocoordinates: A Common Thread for Just About Anything
      1. Wikipedia Articles + Google Maps = Road Trip?
        1. Plotting geo data via microform.at and Google Maps
    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
        1. Frequency by date/time range
        2. Frequency by sender/recipient fields
      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
        1. What entities are in Tim’s tweets?
        2. Do frequently appearing user entities imply friendship?
        3. Splicing in the other half of the conversation
      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
        1. Scalable clustering sure ain’t easy
        2. Intelligent clustering enables compelling user experiences
      4. Hierarchical and k-Means Clustering
        1. Hierarchical clustering
        2. 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
        1. Exploring the Graph API one connection at a time
        2. Slicing and dicing data with FQL
    2. Visualizing Facebook Data
      1. Visualizing Your Entire Social Network
        1. Visualizing with RGraphs
        2. Visualizing with a Sunburst
        3. Visualizing with spreadsheets (the old-fashioned way)
      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