利用数据驱动决策：《Mining the Social Web》第二版

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"《Mining the Social Web(2nd,2013.10)》是Matthew A. Russell撰写的一本书，旨在为特定目标读者群体提供深入的学习体验。书中主要探讨了如何利用数据挖掘技术从社交网络中获取有价值的洞察力。这本书特别关注了在数据驱动的决策背景下，企业和个人如何利用四大趋势所带来的机会：新的数据收集、管理和分析方法，云计算提供的低成本存储和弹性计算能力，复杂数据的可视化技术，以及使数据分析能力普及化的工具。作者通过本书，希望帮助读者掌握处理大数据的方法，并将其转化为具有洞察力的信息。O'Reilly Media, Inc.出版的Strata系列提供了更多关于数据的相关学习资源和灵感，以帮助创新产品、理解用户行为并利用数据获得竞争优势。" 《Mining the Social Web》第二版详细介绍了数据挖掘在社交网络中的应用。它涵盖了以下几个关键知识点： 1. 数据收集与管理：书中讨论了如何在社交媒体环境中有效地收集数据，包括用户生成的内容、互动数据等，并介绍了管理和存储这些大规模数据的方法。 2. 数据分析方法：书中可能涉及统计分析、机器学习算法、自然语言处理等技术，用于从社交网络的海量信息中提取模式、趋势和有意义的关联。 3. 云计算与大数据处理：书中可能会介绍如何利用云计算平台（如Amazon Web Services或Google Cloud Platform）来处理和分析大数据集，强调其经济性和灵活性。 4. 数据可视化：书中的内容可能涵盖各种数据可视化工具和技术，如D3.js，用于将复杂数据转化为易于理解的图像，帮助决策者快速解读数据故事。 5. 数据分析工具：书中会介绍适用于初学者和专业人士的数据分析工具，例如Python库（如Pandas, NumPy, Scikit-learn）和其他开源工具，使非专业人员也能进行数据分析。 6. 社交网络分析：作者可能详细讲解如何分析社交网络结构，如用户关系网络、信息传播路径等，以及如何利用这些分析结果来提升营销策略或预测用户行为。 7. 商业应用：书中可能包含案例研究，展示企业如何运用社交网络数据分析来创建新产品、优化业务流程或提高客户满意度。 8. 隐私与伦理：在进行数据挖掘时，书中可能会讨论如何平衡数据利用与用户隐私，以及遵循的数据伦理原则。 9. 持续学习与更新：由于社交网络和数据科学领域的发展迅速，作者可能会鼓励读者保持学习热情，不断跟踪最新的技术和工具。通过阅读本书，读者不仅可以掌握数据挖掘的基础知识，还能了解到如何将这些知识应用于实际的社交网络数据分析项目，从而实现数据驱动的决策。无论是对于初创公司还是大型企业，掌握这些技能都将带来显著的竞争优势。

to set up a development environment. If you’ve done some programming before, you

should find that it’s relatively painless to get up and running with the code examples.

Even if you’ve never programmed before but consider yourself the least bit tech-savvy,

I daresay that you could use this book as a starting point to a remarkable journey that

will stretch your mind in ways that you probably haven’t even imagined yet.

To fully enjoy this book and all that it has to offer, you need to be interested in the vast

possibilities for mining the rich data tucked away in popular social websites such as

Twitter, Facebook, LinkedIn, and Google+, and you need to be motivated enough to

download a virtual machine and follow along with the book’s example code in IPython

Notebook, a fantastic web-based tool that features all of the examples for every chapter.

Executing the examples is usually as easy as pressing a few keys, since all of the code is

presented to you in a friendly user interface. This book will teach you a few things that

you’ll be thankful to learn and will add a few indispensable tools to your toolbox, but

perhaps even more importantly, it will tell you a story and entertain you along the way.

It’s a story about data science involving social websites, the data that’s tucked away inside

of them, and some of the intriguing possibilities of what you (or anyone else) could do

with this data.

If you were to read this book from cover to cover, you’d notice that this story unfolds

on a chapter-by-chapter basis. While each chapter roughly follows a predictable tem‐

plate that introduces a social website, teaches you how to use its API to fetch data, and

introduces some techniques for data analysis, the broader story the book tells crescendos

in complexity. Earlier chapters in the book take a little more time to introduce funda‐

mental concepts, while later chapters systematically build upon the foundation from

earlier chapters and gradually introduce a broad array of tools and techniques for mining

the social web that you can take with you into other aspects of your life as a data scientist,

analyst, visionary thinker, or curious reader.

Some of the most popular social websites have transitioned from fad to mainstream to

household names over recent years, changing the way we live our lives on and off the

Web and enabling technology to bring out the best (and sometimes the worst) in us.

Generally speaking, each chapter of this book interlaces slivers of the social web along

with data mining, analysis, and visualization techniques to explore data and answer the

following representative questions:

•

Who knows whom, and which people are common to their social networks?

•

How frequently are particular people communicating with one another?

•

Which social network connections generate the most value for a particular niche?

•

How does geography affect your social connections in an online world?

xiv | Preface

• Who are the most influential/popular people in a social network?

• What are people chatting about (and is it valuable)?

• What are people interested in based upon the human language that they use in a

digital world?

The answers to these basic kinds of questions often yield valuable insight and present

lucrative opportunities for entrepreneurs, social scientists, and other curious practi‐

tioners who are trying to understand a problem space and find solutions. Activities such

as building a turnkey killer app from scratch to answer these questions, venturing far

beyond the typical usage of visualization libraries, and constructing just about anything

state-of-the-art are not within the scope of this book. You’ll be really disappointed if

you purchase this book because you want to do one of those things. However, this book

does provide the fundamental building blocks to answer these questions and provide a

springboard that might be exactly what you need to build that killer app or conduct that

research study. Skim a few chapters and see for yourself. This book covers a lot of ground.

Python-Centric Technology

This book intentionally takes advantage of the Python programming language for all of

its example code. Python’s intuitive syntax, amazing ecosystem of packages that trivialize

API access and data manipulation, and core data structures that are practically JSON

make it an excellent teaching tool that’s powerful yet also very easy to get up and running.

As if that weren’t enough to make Python both a great pedagogical choice and a very

pragmatic choice for mining the social web, there’s IPython Notebook, a powerful, in‐

teractive Python interpreter that provides a notebook-like user experience from within

your web browser and combines code execution, code output, text, mathematical type‐

setting, plots, and more. It’s difficult to imagine a better user experience for a learning

environment, because it trivializes the problem of delivering sample code that you as

the reader can follow along with and execute with no hassles. Figure P-1 provides an

illustration of the IPython Notebook experience, demonstrating the dashboard of note‐

books for each chapter of the book. Figure P-2 shows a view of one notebook.

Preface | xv

Every chapter in this book has a corresponding IPython Notebook with example code

that makes it a pleasure to study the code, tinker around with it, and customize it for

your own purposes. If you’ve done some programming but have never seen Python

syntax, skimming ahead a few pages should hopefully be all the confirmation that you

need. Excellent documentation is available online, and the official Python tutorial is a

good place to start if you’re looking for a solid introduction to Python as a programming

language. This book’s Python source code is written in Python 2.7, the latest release of

the 2.x line. (Although perhaps not entirely trivial, it’s not too difficult to imagine using

some of the automated tools to up-convert it to Python 3 for anyone who is interested

in helping to make that happen.)

IPython Notebook is great, but if you’re new to the Python programming world, advising

you to just follow the instructions online to configure your development environment

would be a bit counterproductive (and possibly even rude). To make your experience

with this book as enjoyable as possible, a turnkey virtual machine is available that has

IPython Notebook and all of the other dependencies that you’ll need to follow along

with the examples from this book preinstalled and ready to go. All that you have to do

is follow a few simple steps, and in about 15 minutes, you’ll be off to the races. If you

have a programming background, you’ll be able to configure your own development

environment, but my hope is that I’ll convince you that the virtual machine experience

is a better starting point.

See Appendix A for more detailed information on the virtual ma‐

chine experience for this book. Appendix C is also worth your atten‐

tion: it presents some IPython Notebook tips and common Python

programming idioms that are used throughout this book’s source code.

Whether you’re a Python novice or a guru, the book’s latest bug-fixed source code and

accompanying scripts for building the virtual machine are available on GitHub, a social

Git repository that will always reflect the most up-to-date example code available. The

hope is that social coding will enhance collaboration between like-minded folks who

want to work together to extend the examples and hack away at fascinating problems.

Hopefully, you’ll fork, extend, and improve the source—and maybe even make some

new friends or acquaintances along the way.

The official GitHub repository containing the latest and greatest bug-

fixed source code for this book is available at http://bit.ly/MiningThe

SocialWeb2E.

Preface | xvii

Improvements Specific to the Second Edition

When I began working on this second edition of Mining the Social Web, I don’t think I

quite realized what I was getting myself into. What started out as a “substantial update”

is now what I’d consider almost a rewrite of the first edition. I’ve extensively updated

each chapter, I’ve strategically added new content, and I really do believe that this second

edition is superior to the first in almost every way. My earnest hope is that it’s going to

be able to reach a much wider audience than the first edition and invigorate a broad

community of interest with tools, techniques, and practical advice to implement ideas

that depend on munging and analyzing data from social websites. If I am successful in

this endeavor, we’ll see a broader awareness of what it is possible to do with data from

social websites and more budding entrepreneurs and enthusiastic hobbyists putting

social web data to work.

A book is a product, and first editions of any product can be vastly improved upon,

aren’t always what customers ideally would have wanted, and can have great potential

if appropriate feedback is humbly accepted and adjustments are made. This book is no

exception, and the feedback and learning experience from interacting with readers and

consumers of this book’s sample code over the past few years have been incredibly

important in shaping this book to be far beyond anything I could have designed if left

to my own devices. I’ve incorporated as much of that feedback as possible, and it mostly

boils down to the theme of simplifying the learning experience for readers.

Simplification presents itself in this second edition in a variety of ways. Perhaps most

notably, one of the biggest differences between this book and the previous edition is

that the technology toolchain is vastly simplified, and I’ve employed configuration

management by way of an amazing virtualization technology called Vagrant. The pre‐

vious edition involved a variety of databases for storage, various visualization toolkits,

and assumed that readers could just figure out most of the installation and configuration

by reading the online instructions.

This edition, on the other hand, goes to great lengths to introduce as few disparate

technology dependencies as possible and presents them all with a virtual machine ex‐

perience that abstracts away the complexities of software installation and configuration,

which are sometimes considerably more challenging than they might initially seem.

From a certain vantage point, the core toolbox is just IPython Notebook and some third-

party package dependencies (all of which are versioned so that updates to open source

software don’t cause code breakage) that come preinstalled on a virtual machine. Inline

visualizations are even baked into the IPython Notebooks, rendering from within IPy‐

thon Notebook itself, and are consolidated down to a single JavaScript toolkit (D3.js)

that maintains visually consistent aesthetics across the chapters.

xviii | Preface

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