21 Recipes for Mining Twitter: Distilling Rich Information from Messy Data

by Matthew A. Russell

Overview

Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs. The recipes include techniques to:

• Use OAuth to access Twitter data
• Create and analyze graphs of retweet relationships
• Use the streaming API to harvest tweets in realtime
• Harvest and analyze friends and followers
• Discover friendship cliques
• Summarize webpages from short URLs

This book is a perfect companion to O’Reilly's Mining the Social Web.

Product Details

ISBN-13:	9781449303853
Publisher:	O'Reilly Media, Incorporated
Publication date:	01/31/2011
Sold by:	Barnes & Noble
Format:	eBook
Pages:	76
File size:	1 MB

About the Author

Matthew Russell, Vice President of Engineering at Digital Reasoning Systems (http://www.digitalreasoning.com/) and Principal at Zaffra (http://zaffra.com), is a computer scientist who is passionate about data mining, open source, and web application technologies. He’s also the author of Dojo: The Definitive Guide (O’Reilly).

Table of Contents

Preface;
Introduction;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Chapter 1: The Recipes;
1.1 Using OAuth to Access Twitter APIs;
1.2 Looking Up the Trending Topics;
1.3 Extracting Tweet Entities;
1.4 Searching for Tweets;
1.5 Extracting a Retweet’s Origins;
1.6 Creating a Graph of Retweet Relationships;
1.7 Visualizing a Graph of Retweet Relationships;
1.8 Capturing Tweets in Real-time with the Streaming API;
1.9 Making Robust Twitter Requests;
1.10 Harvesting Tweets;
1.11 Creating a Tag Cloud from Tweet Entities;
1.12 Summarizing Link Targets;
1.13 Harvesting Friends and Followers;
1.14 Performing Setwise Operations on Friendship Data;
1.15 Resolving User Profile Information;
1.16 Crawling Followers to Approximate Potential Influence;
1.17 Analyzing Friendship Relationships such as Friends of Friends;
1.18 Analyzing Friendship Cliques;
1.19 Analyzing the Authors of Tweets that Appear in Search Results;
1.20 Visualizing Geodata with a Dorling Cartogram;
1.21 Geocoding Locations from Profiles (or Elsewhere);