社交网络数据挖掘：揭示隐藏的社交模式

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"Mining the Social Web" 是一本关于利用数据挖掘技术探索社交媒体信息的书籍。作者通过这本书揭示了社交网络中的海量数据背后所蕴含的价值，并提供了API接口使用、数据分析以及洞察力提取的方法。这本书受到了业界专家的高度评价，被认为是对数据科学家、社交媒体爱好者和API开发者极有帮助的入门指南。在描述中，提到了万维网（Web）的设计初衷是促进人们的协作，而不是作为一个单纯的科技玩具。Web的目标是支持和改善人们在现实世界中的社交关系，包括家庭、协会和公司等多方面的联系。Tim Berners-Lee，即万维网的创始人，强调了信任在网络中的重要性，这为数据挖掘提供了社会学的背景。标签"Data"表明本书的核心内容是围绕数据展开的，特别是社交媒体上的数据。书中详细介绍了如何访问这些数据源，以及如何运用简单的方法从原始数据中提取有价值的见解。部分内容摘录中，多位评论家对《Mining the Social Web》给予了高度赞扬，认为它是理解和分析社交媒体数据的必备读物。书中不仅包含了大量实用的例子，还涵盖了从获取数据到提取洞察力的整个过程，适合从初学者到高级用户的各类读者。书中的内容可能包括以下几个方面： 1. API接口与社交媒体：讲解如何使用API来获取社交媒体平台如Facebook、Twitter或Instagram等的数据。 2. 数据处理基础：介绍数据清洗、预处理和转换的基本步骤，以便进行后续分析。 3. 数据分析工具：介绍Python等编程语言中的数据挖掘库，如Pandas、Numpy和Scikit-learn等，以及如何利用它们处理社交数据。 4. 社交网络分析：探讨社交网络结构，如用户之间的关系网络，以及如何使用图论方法进行分析。 5. 文本挖掘：教授如何分析社交媒体文本，包括情感分析、主题建模和关键词提取等。 6. 数据可视化：展示如何将数据转化为直观的图表，帮助理解社交网络中的模式和趋势。 7. 实例与项目：提供实际案例，让读者能够跟随操作，学习如何从头到尾完成一个社交数据分析项目。《Mining the Social Web》是一本深度剖析社交网络数据的实践指南，旨在帮助读者掌握在大数据时代如何从社交媒体的海洋中淘金，发现有价值的信息，并利用这些信息做出有洞察力的决策。无论是对个人还是企业，掌握这些技能都将对理解和利用社交网络的影响力有着深远的意义。

Social networking websites such as Facebook, Twitter, and LinkedIn have transitioned

from fad to mainstream to global phenomena over the last few years. In the first quarter

of 2010, the popular social networking site Facebook surpassed Google for the most

page visits,

confirming a definite shift in how people are spending their time online.

Asserting that this event indicates that the Web has now become more a social milieu

than a tool for research and information might be somewhat indefensible; however,

this data point undeniably indicates that social networking websites are satisfying some

very basic human desires on a massive scale in ways that search engines were never

designed to fulfill. Social networks really are changing the way we live our lives on and

off the Web,

†

and they are enabling technology to bring out the best (and sometimes

the worst) in us. The explosion of social networks is just one of the ways that the gap

between the real world and cyberspace is continuing to narrow.

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

with data mining, analysis, and visualization techniques to answer the following kinds

of questions:

• Who knows whom, and what friends do they have in common?

• How frequently are certain people communicating with one another?

• How symmetrical is the communication between people?

• Who are the quietest/chattiest people in a network?

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

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

The answers to these types of questions generally connect two or more people together

and point back to a context indicating why the connection exists. The work involved

in answering these kinds of questions is only the beginning of more complex analytic

processes, but you have to start somewhere, and the low-hanging fruit is surprisingly

easy to grasp, thanks to well-engineered social networking APIs and open source

toolkits.

Loosely speaking, this book treats the social web

‡

as a graph of people, activities, events,

concepts, etc. Industry leaders such as Google and Facebook have begun to increasingly

push graph-centric terminology rather than web-centric terminology as they simulta-

neously promote graph-based APIs. In fact, Tim Berners-Lee has suggested that perhaps

he should have used the term Giant Global Graph (GGG) instead of World Wide Web

(WWW), because the terms “web” and “graph” can be so freely interchanged in the

context of defining a topology for the Internet. Whether the fullness of Tim Berners-

* See the opening paragraph of Chapter 9.

† Mark Zuckerberg, the creator of Facebook, was named Person of the Year for 2010 by Time magazine (http:

//www.time.com/time/specials/packages/article/0,28804,2036683_2037183_2037185,00.html)

‡ See http://journal.planetwork.net/article.php?lab=reed0704 for another perspective on the social web that

focuses on digital identities.

xiv | Preface

Lee’s original vision will ever be realized remains to be seen, but the Web as we know

it is getting richer and richer with social data all the time. When we look back years

from now, it may well seem obvious that the second- and third-level effects created by

an inherently social web were necessary enablers for the realization of a truly semantic

web. The gap between the two seems to be closing.

Or Not to Read This Book?

Activities such as building your own natural language processor from scratch, venturing

far beyond the typical usage of visualization libraries, and constructing just about any-

thing 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, just

because it’s not realistic or our goal to capture the holy grail of text analytics or record

matching in a mere few hundred pages doesn’t mean that this book won’t enable you

to attain reasonable solutions to hard problems, apply those solutions to the social web

as a domain, and have a lot of fun in the process. It also doesn’t mean that taking a very

active interest in these fascinating research areas wouldn’t potentially be a great idea

for you to consider. A short book like this one can’t do much beyond whetting your

appetite and giving you enough insight to go out and start making a difference some-

where with your newly found passion for data hacking.

Maybe it’s obvious in this day and age, but another important item of note is that this

book generally assumes that you’re connected to the Internet. This wouldn’t be a great

book to take on vacation with you to a remote location, because it contains many

references that have been hyperlinked, and all of the code examples are hyperlinked

directly to GitHub, a very 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 such as ourselves who want to work together to extend the

examples and hack away at interesting problems. Hopefully, you’ll fork, extend, and

improve the source—and maybe even make some new friends along the way. Readily

accessible sources of online information such as API docs are also liberally hyperlinked,

and it is assumed that you’d rather look them up online than rely on inevitably stale

copies in this printed book.

The official GitHub repository that maintains the latest and greatest

bug-fixed source code for this book is http://github.com/ptwobrussell/

Mining-the-Social-Web. The official Twitter account for this book is

@SocialWebMining.

This book is also not recommended if you need a reference that gets you up to speed

on distributed computing platforms such as sharded MySQL clusters or NoSQL tech-

nologies such as Hadoop or Cassandra. We do use some less-than-conventional storage

technologies such as CouchDB and Redis, but always within the context of running on

Preface | xv

a single machine, and because they work well for the problem at hand. However, it

really isn’t that much of a stretch to port the examples into distributed technologies if

you possess sufficient motivation and need the horizontal scalability. A strong recom-

mendation is that you master the fundamentals and prove out your thesis in a slightly

less complex environment first before migrating to an inherently more complex dis-

tributed system—and then be ready to make major adjustments to your algorithms to

make them performant once data access is no longer local. A good option to investigate

if you want to go this route is Dumbo. Stay tuned to this book’s Twitter account

(@SocialWebMining) for extended examples that involve Dumbo.

This book provides no advice whatsoever about the legal ramifications of what you

may decide to do with the data that’s made available to you from social networking

sites, although it does sincerely attempt to comply with the letter and spirit of the terms

governing the particular sites that are mentioned. It may seem unfortunate that many

of the most popular social networking sites have licensing terms that prohibit the use

of their data outside of their platforms, but at the moment, it’s par for the course. Most

social networking sites are like walled gardens, but from their standpoint (and the

standpoint of their investors) a lot of the value these companies offer currently relies

on controlling the platforms and protecting the privacy of their users; it’s a tough bal-

ance to maintain and probably won’t be all sorted out anytime soon.

A final and much lesser caveat is that this book does slightly favor a *nix environ-

ment,

in that there are a select few visualizations that may give Windows users trouble.

Whenever this is known to be a problem, however, advice is given on reasonable al-

ternatives or workarounds, such as firing up a VirtualBox to run the example in a Linux

environment. Fortunately, this doesn’t come up often, and the few times it does you

can safely ignore those sections and move on without any substantive loss of reading

enjoyment.

Tools and Prerequisites

The only real prerequisites for this book are that you need to be motivated enough to

learn some Python and have the desire to get your hands (really) dirty with social data.

None of the techniques or examples in this book require significant background knowl-

edge of data analysis, high performance computing, distributed systems, machine

learning, or anything else in particular. Some examples involve constructs you may not

have used before, such as thread pools, but don’t fret—we’re programming in Python.

Python’s intuitive syntax, amazing ecosystem of packages for 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. On other occasions we use some

packages that do pretty advanced things, such as processing natural language, but we’ll

§ *nix is a term used to refer to a Linux/Unix environment, which is basically synonymous with non-Windows

at this point in time.

xvi | Preface

Do wn lo ad f r o m W o w ! e B oo k <w ww .w ow e b o o k . c o m >

approach these from the standpoint of using the technology as an application pro-

grammer. Given the high likelihood that very similar bindings exist for other program-

ming languages, it should be a fairly rote exercise to port the code examples should you

so desire. (Hopefully, that’s exactly the kind of thing that will happen on GitHub!)

Beyond the previous explanation, this book makes no attempt to justify the selection

of Python or apologize for using it, because it’s a very suitable tool for the job. If you’re

new to programming or 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.

This book attempts to introduce a broad array of useful visualizations across a variety

of visualization tools and toolkits, ranging from consumer staples like spreadsheets to

industry staples like Graphviz, to bleeding-edge HTML5 technologies such as Proto-

vis. A reasonable attempt has been made to introduce a couple of new visualizations

in each chapter, but in a way that follows naturally and makes sense. You’ll need to be

comfortable with the idea of building lightweight prototypes from these tools. That

said, most of the visualizations in this book are little more than small mutations on out-

of-the-box examples or projects that minimally exercise the APIs, so as long as you’re

willing to learn, you should be in good shape.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Indicates program listings, and is used within paragraphs to refer to program

elements such as variable or function names, databases, data types, environment

variables, statements, and keywords.

Constant width bold

Shows commands or other text that should be typed literally by the user. Also

occasionally used for emphasis in code listings.

Constant width italic

Shows text that should be replaced with user-supplied values or values determined

by context.

This icon signifies a tip, suggestion, or general note.

Preface | xvii

This icon indicates a warning or caution.

Using Code Examples

Most of the numbered examples in the following chapters are available for download

at GitHub at https://github.com/ptwobrussell/Mining-the-Social-Web—the official code

repository for this book. You are encouraged to monitor this repository for the latest

bug-fixed code as well as extended examples by the author and the rest of the social

coding community.

This book is here to help you get your job done. In general, you may use the code in

this book in your programs and documentation. You do not need to contact us for

permission unless you’re reproducing a significant portion of the code. For example,

writing a program that uses several chunks of code from this book does not require

permission. Selling or distributing a CD-ROM of examples from O’Reilly books does

require permission. Answering a question by citing this book and quoting example

code does not require permission. Incorporating a significant amount of example code

from this book into your product’s documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title,

author, publisher, and ISBN. For example: “Mining the Social Web by Matthew A.

If you feel your use of code examples falls outside fair use or the permission given above,

feel free to contact us at permissions@oreilly.com.

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xviii | Preface

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社交网络数据挖掘：揭示隐藏的社交模式

Mining the Social web

Mining the social web

mining the social web

[挖掘社交网络].(Mining.the.Social.Web).Matthew.A.Russell.文字版

[Mining.the.Social.Web(2nd,2013.10)].Matthew.A.Russell.文字版.pdf

CRC.Social.Big.Data.Mining.149871093X

R.Data.Mining.Projects.1783989688

Mining the Social Web

Mining the Social Web 1st Edition By Matthew A. Russell

https-github.com-ptwobrussell-Mining-the-Social-Web-2nd-Edition:挖掘-社交-网络

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