具体描述
《挖掘社交网络(影印版)》，本书简洁而且具有操作性的书将为你展示如何回答这些甚至更多的问题，你将学到如何组合社交网络数据、分析技术，如何通过可视化帮助你找到你一直在社交世界中的内容。
迷失的灯塔：一座失落城市的考古发现与人类文明的再思考 作者： 艾米莉亚·凡尔纳 出版社： 恒星视野文化 页码： 680页 定价： 128.00 元 --- 内容提要： 《迷失的灯塔》并非一部关于现代社会连接与互动的论著，它是一部沉浸式的、对人类文明早期形态的深度考古报告与哲学思辨录。本书聚焦于南太平洋深处一个被称为“阿卡迪亚”（Arcadia）的失落水下城市遗址的发掘工作。阿卡迪亚的发现，彻底颠覆了我们对史前文明技术水平、社会结构乃至宇宙观的既有认知。 作者艾米莉亚·凡尔纳，一位以其大胆的理论和严谨的田野工作著称的古文明学家，带领读者穿梭于深海的幽暗与文明的残骸之间。她不仅详尽记录了从海底沉积物中抢救出的精密仪器、非欧几里得几何形态的建筑结构，以及迄今为止无法破译的象形符号系统，更重要的是，她试图从这些冰冷的遗物中，重构一个已经消亡的社会赖以生存的“逻辑”。 本书的核心在于探讨“衰落的必然性”。阿卡迪亚文明，在技术和哲学上似乎远超我们现有的理解，却在一个似乎平静的“大寂静”时期神秘终结。凡尔纳教授没有提供一个简单的灾难叙事，而是深入剖析了阿卡迪亚社会内部可能存在的“系统性熵增”——一个看似完美、高度自洽的文明，是如何因为其内在的结构性缺陷而走向自我瓦解的。书中详细分析了他们的资源分配模型、对“无限增长”概念的摒弃，以及他们在艺术与科学领域对“有限性”的极致探索。 读者将跟随考古队的足迹，体验从初步声纳扫描到最终进入核心神庙的每一个紧张时刻。书中穿插了大量由现场绘制的素描、对出土文物的科学分析报告摘要，以及与顶尖语言学家、物理学家关于这些遗物的激辩记录。 《迷失的灯塔》是一次对“进步”概念的深刻质疑，它强迫我们审视：我们今天所珍视的连接、效率和信息流动，是否恰恰是通往另一种形式的“阿卡迪亚式”终结的铺路石？这部作品不仅是考古学的里程碑，更是一部关于人类命运与文明边界的警世之作。它邀请所有对历史、哲学、以及我们自身的未来感到好奇的读者，一同潜入深海，面对那座曾经光芒万丈，最终熄灭的灯塔。 --- 详细内容章节概要（节选）： 第一部分：深海的呼唤与初探（Foundations and First Contact） 1. 声纳的低语： 详细描述了“海燕号”考察队首次捕捉到阿卡迪亚异常几何结构信号的整个过程。对当时船上科学家们从最初的不信任到逐步确认发现了一个巨大人造结构群落的心理轨迹进行了细致描摹。 2. 深海的屏障： 描述了首次潜水任务的巨大技术挑战。阿卡迪亚城市遗址位于马里亚纳海沟边缘，水压、黑暗与未知的生物威胁如何考验着现代潜水技术。此处重点描写了首次接触到城市外围“防护墙”的震撼感，其材料的未知属性和光滑程度令人难以置信。 3. 时间胶囊与零号遗址： 聚焦于发掘出的第一个保存相对完整的“起居模块”。分析了模块内壁上雕刻的日历系统，该系统与已知的任何天文模型都不符，暗示了阿卡迪亚人对时间和宇宙运行模式有着截然不同的理解。 第二部分：结构与符号的迷宫（The Architecture of Oblivion） 4. 非欧几里得的穹顶： 本章是建筑分析的重点。详细对比了阿卡迪亚城中标志性的螺旋上升结构与现代建筑学中的逻辑。作者提出一个大胆的假设：这些建筑并非为了居住，而是作为某种宏大的“信息存储或传输设备”。配有由考古绘制的复杂平面图解。 5. 沉默的语言： 深入探讨了出土的数千块刻有符号的“记忆石板”。语言学家团队尝试用所有已知的人类语系（包括已灭绝的文明语言）进行比对均告失败。凡尔纳教授根据符号出现的频率和上下文推测，这可能是一种高度抽象化、甚至与情绪或意识状态直接挂钩的符号系统，而非记录事件的语言。 6. 能量的残影： 报告了对城市中央能源核心区域的勘探结果。没有发现任何化石燃料或核裂变迹象，相反，发现了大量由稳定同位素构成的晶体结构，它们似乎能以极低的损失率储存和释放能量。本章对这些“永恒晶体”的物理特性进行了严谨的科学讨论，但承认其运作原理远超当前物理学认知。 第三部分：文明的哲学与内在矛盾（Philosophy and System Failure） 7. “盈满”的禁忌： 通过解读部分可部分还原的壁画残片，作者揭示了阿卡迪亚社会的核心伦理观——他们对“过度积累”持有一种近乎宗教般的恐惧。他们的社会分配系统似乎旨在将所有资源的流动性维持在一个极低的、刚能维持运作的水平。 8. 知识的隔离墙： 令人不安的发现是，阿卡迪亚的知识和技术并非普惠的。作者发现了一系列被刻意封存的、涉及更高阶物理学和时间观测的“禁忌室”。这些房间的入口被复杂的、似乎是自我销毁的防御机制所保护，暗示了文明内部存在重大的知识分歧或对技术失控的深层恐惧。 9. “大寂静”的真相： 这是本书最具争议的一章。通过对城市内最后居住区域的残留物分析，凡尔纳排除了自然灾害、战争或瘟疫的可能。她倾向于一个更微妙的结论：阿卡迪亚文明并非被“摧毁”，而是“自行选择了停止”。他们可能在达到某种哲学上的“圆满”或“认知饱和”后，主动放弃了继续存在的必要性。书中详细论述了他们自我终止的可能路径，这涉及对个体意识与集体记忆的最终处理。 结语：回望我们自己的灯塔 凡尔纳教授以沉重的笔触结束全书：阿卡迪亚的遗址像一面镜子，映照出我们这个快速扩张、信息爆炸的时代。我们是否正在以更快的速度接近我们自身文明的“认知饱和点”？这本书不仅是对一个失落文明的致敬，更是对当代人类如何定义“生存”、“进步”和“终结”的深刻反思。 --- 推荐人群： 对深海考古学、史前文明感兴趣的专业研究人员。 喜爱宏大叙事、哲学思辨和反乌托邦设想的严肃文学爱好者。 对现代社会发展模式持批判性态度的思想者。
作者简介
马修·罗塞尔（Matthew A.Russell），Digital Reasoning Systems公司的技术副总裁和Zaffra公司的负责人，是热爱数据挖掘、开源和Web应用技术的计算机科学家。他也是《Dojo: The Dofinitive Guide》（O'Reilly出版社）的作者。在LinkedIn上联系他或在Twitter上关注@ptwobrussell，可随时关注他的最新动态。
目录信息
preface
1. introduction: hacking on twitter data
installing python development tools
collecting and manipulating twitter data
tinkering with twitter's apl
frequency analysis and lexical diversity
visualizing tweet graphs
synthesis: visualizing retweets with protovis
closing remarks
2. microformats: semantic markup and common sense collide
xfn and friends
exploring social connections with xfn
a breadth-first crawl of xfn data
geocoordinates: a common thread for just about anything
wikipedia articles + google maps = road trip?
slicing and dicing recipes (for the health of it)
collecting restaurant reviews
summary
3. mailboxes: oldies but goodies
.mbox: the quick and dirty on unix mailboxes
mbox + couchdb = relaxed email analysis
bulk loading documents into couchdb
sensible sorting
map/reduce-inspired frequency analysis
sorting documents by value
couchdb-lucene: full-text indexing and more
threading together conversations
look who's talking
visualizing mail "events" with simile timeline
analyzing your own mail data
the graph your (gmail) inbox chrome extension
closing remarks
4. twitter: friends, followers, and setwise operations
restful and oauth-cladded apis
no, you can't have my password
a lean, mean data-collecting machine
a very brief refactor interlude
redis: a data structures server
elementary set operations
souping up the machine with basic friend/follower metrics
calculating similarity by computing common friends and followers
measuring influence
constructing friendship graphs
clique detection and analysis
the infochimps "strong links" apl
interactive 3d graph visualization
summary
5. twitter: the tweet, the whole tweet, and nothing but the tweet
pen: sword∷ tweet: machine gun (?!?)
analyzing tweets (one entity at a time)
tapping (tim's) tweets
who does tim retweet most often?
what's tim's influence?
how many of tim's tweets contain hashtags?
juxtaposing latent social networks (or #justinbieber versus #teaparty)
what entities co-occur most often with #justinbieber and #teaparty
tweets?
on average, do #justinbieber or #teaparty tweets have more
hashtags?
which gets retweeted more often: #justinbieber or #teaparty?
how much overlap exists between the entities of #teaparty and
#justinbieber tweets?
visualizing tons of tweets
visualizing tweets with tricked-out tag clouds
visualizing community structures in twitter search results
closing remarks
6. linkedln: clustering your professional network for fun (and profit?)
motivation for clustering
clustering contacts by job title
standardizing and counting job titles
common similarity metrics for clustering
a greedy approach to clustering
hierarchical and k-means clustering
fetching extended profile information
geographically clustering your network
mapping your professional network with google earth
mapping your professional network with dorling cartograms
closing remarks
?. 6oogle buzz: tf-idf, cosine similarity, and collocations
buzz = twitter + blogs (???)
data hacking with nltk
text mining fundamentals
a whiz-bang introduction to tf-idf
querying buzz data with tf-idf
finding similar documents
the theory behind vector space models and cosine similarity
clustering posts with cosine similarity
visualizing similarity with graph visualizations
buzzing on bigrams
how the collocation sausage is made: contingency tables and scoring
functions
tapping into your gmail
accessing gmail with oauth
fetching and parsing email messages
before you go off and try to build a search engine.
closing remarks
8. blogs et al.: natural language processing (and beyond)
nlp: a pareto-like introduction
syntax and semantics
a brief thought exercise
a typical nlp pipeline with nltk
sentence detection in blogs with nltk
summarizing documents
analysis of luhn's summarization algorithm
entity-centric analysis: a deeper understanding of the data
quality of analytics
closing remarks
9. facebook: the all-in-one wonder
tapping into your social network data
from zero to access token in under 10 minutes
facebook's query apis
visualizing facebook data
visualizing your entire social network
visualizing mutual friendships within groups
where have my friends all gone? (a data-driven game)
visualizing wall data as a (rotating) tag cloud
closing remarks
10. the semantic web: a cocktail discussion
an evolutionary revolution?
man cannot live on facts alone
open-world versus closed-world assumptions
inferencing about an open world with fuxi
hope
index
· · · · · · (收起)
1. introduction: hacking on twitter data
installing python development tools
collecting and manipulating twitter data
tinkering with twitter's apl
frequency analysis and lexical diversity
visualizing tweet graphs
synthesis: visualizing retweets with protovis
closing remarks
2. microformats: semantic markup and common sense collide
xfn and friends
exploring social connections with xfn
a breadth-first crawl of xfn data
geocoordinates: a common thread for just about anything
wikipedia articles + google maps = road trip?
slicing and dicing recipes (for the health of it)
collecting restaurant reviews
summary
3. mailboxes: oldies but goodies
.mbox: the quick and dirty on unix mailboxes
mbox + couchdb = relaxed email analysis
bulk loading documents into couchdb
sensible sorting
map/reduce-inspired frequency analysis
sorting documents by value
couchdb-lucene: full-text indexing and more
threading together conversations
look who's talking
visualizing mail "events" with simile timeline
analyzing your own mail data
the graph your (gmail) inbox chrome extension
closing remarks
4. twitter: friends, followers, and setwise operations
restful and oauth-cladded apis
no, you can't have my password
a lean, mean data-collecting machine
a very brief refactor interlude
redis: a data structures server
elementary set operations
souping up the machine with basic friend/follower metrics
calculating similarity by computing common friends and followers
measuring influence
constructing friendship graphs
clique detection and analysis
the infochimps "strong links" apl
interactive 3d graph visualization
summary
5. twitter: the tweet, the whole tweet, and nothing but the tweet
pen: sword∷ tweet: machine gun (?!?)
analyzing tweets (one entity at a time)
tapping (tim's) tweets
who does tim retweet most often?
what's tim's influence?
how many of tim's tweets contain hashtags?
juxtaposing latent social networks (or #justinbieber versus #teaparty)
what entities co-occur most often with #justinbieber and #teaparty
tweets?
on average, do #justinbieber or #teaparty tweets have more
hashtags?
which gets retweeted more often: #justinbieber or #teaparty?
how much overlap exists between the entities of #teaparty and
#justinbieber tweets?
visualizing tons of tweets
visualizing tweets with tricked-out tag clouds
visualizing community structures in twitter search results
closing remarks
6. linkedln: clustering your professional network for fun (and profit?)
motivation for clustering
clustering contacts by job title
standardizing and counting job titles
common similarity metrics for clustering
a greedy approach to clustering
hierarchical and k-means clustering
fetching extended profile information
geographically clustering your network
mapping your professional network with google earth
mapping your professional network with dorling cartograms
closing remarks
?. 6oogle buzz: tf-idf, cosine similarity, and collocations
buzz = twitter + blogs (???)
data hacking with nltk
text mining fundamentals
a whiz-bang introduction to tf-idf
querying buzz data with tf-idf
finding similar documents
the theory behind vector space models and cosine similarity
clustering posts with cosine similarity
visualizing similarity with graph visualizations
buzzing on bigrams
how the collocation sausage is made: contingency tables and scoring
functions
tapping into your gmail
accessing gmail with oauth
fetching and parsing email messages
before you go off and try to build a search engine.
closing remarks
8. blogs et al.: natural language processing (and beyond)
nlp: a pareto-like introduction
syntax and semantics
a brief thought exercise
a typical nlp pipeline with nltk
sentence detection in blogs with nltk
summarizing documents
analysis of luhn's summarization algorithm
entity-centric analysis: a deeper understanding of the data
quality of analytics
closing remarks
9. facebook: the all-in-one wonder
tapping into your social network data
from zero to access token in under 10 minutes
facebook's query apis
visualizing facebook data
visualizing your entire social network
visualizing mutual friendships within groups
where have my friends all gone? (a data-driven game)
visualizing wall data as a (rotating) tag cloud
closing remarks
10. the semantic web: a cocktail discussion
an evolutionary revolution?
man cannot live on facts alone
open-world versus closed-world assumptions
inferencing about an open world with fuxi
hope
index
· · · · · · (收起)
读后感
Popular social networks such as Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data. Who's talking to whom? What are they talking about? How often are they talking? Where are they located? This concise and practical book sho...
评价给的是原书的。 本来是一本还不错的书，看着那些翻译的语句，哎，真操蛋，这是我直接扔垃圾桶的第一本书，翻译的真不行。 再说书的内容，大概过了一遍，内容挺丰富的，包括了邮件、twitter、facebook、linkedin等各个方面的挖掘想法、工具，还是不错的。对于数据分析的关键...
yes, damn beaver -,-# 社交网站的DM需要用直推来隐藏看似复杂却又简单，做起来简单却确实不是随便谁都能做好的工作。 UPLOAD YOUR SOUL TO THE ULTIMATE INTERNET！哈哈哈哈！
Facebook、Twitter和LinkedIn产生了大量宝贵的社交数据，但是你怎样才能找出谁通过社交媒介正在进行联系?他们在讨论些什么?或者他们在哪儿?这本简洁而且具有可操作性的书将揭示如何回答这些问题甚至更多的问题。你将学到如何组合社交网络数据、分析技术，如何通过可视化帮助你...
如果你希望从这本书里边学到任何软件使用方法以外的东西，我觉得你会失望的。 因为从第七章开始才讲算法，还将得各种悲剧。直接看wikipedia都能理解得更快。 之前的章节都是各个社交网络API的介绍和工具使用介绍，还算行吧。 里边提到的工具目录里边基本都有，直接上官方站...
用户评价
做挖掘的入门好书，如果你想挖掘其它的，启发也不小。
做挖掘的入门好书，如果你想挖掘其它的，启发也不小。
做挖掘的入门好书，如果你想挖掘其它的，启发也不小。
做挖掘的入门好书，如果你想挖掘其它的，启发也不小。
做挖掘的入门好书，如果你想挖掘其它的，启发也不小。
