更改

元胞自动机范式引发了强调'''基于主体建模 Agent-based Modeling'''的第三次社会模拟浪潮。与微观模拟一样，这些模型强调自下而上的设计，但采用了与微观模拟不同的四个关键假设: '''自主性 Autonomy'''、'''相互依赖性 Interdependency'''、'''简单规则 Simple Rules'''和'''适应性行为 Adaptive Behavior'''。1981年，数学家、政治学家阿克塞尔罗德（Robert Axelrod）和进化生物学家汉密尔顿（W.D. Hamilton）在《科学》杂志上发表了一篇名为《合作的进化》（The Evolution of Cooperation）的重要论文，该论文采用基于主体建模方法，论证了在一个囚徒困境博弈中，当主体遵循简单的自利规则时，互惠的社会合作是如何建立和稳定的。阿克塞尔罗德和汉密尔顿证明，主体遵循这样一套简单的规则: (1)在第一轮进行合作，(2)在其后重复伙伴以前的行动，能够在没有人口学差异、价值观、宗教和文化等社会规范作为合作的先决条件或中介的情况下制定合作和制裁的“规范”。整个20世纪90年代，像威廉·希姆斯·本布里奇（William Sims Bainbridge），Kathleen Carley，Michael Macy 和 John Skvoretz 这样的学者开发了基于多主体的广义互惠、偏见、社会影响和组织信息处理模型。1999年，吉尔伯特（Nigel Gilbert）出版了第一本关于社会模拟: 写给社会科学家的仿真模拟（Simulation for the social scientist）的教科书，并建立了它最相关的杂志: 人工社会和社会模拟杂志（the Journal of Artificial Societies and Social Simulation）。

元胞自动机范式引发了强调'''基于主体建模 Agent-based Modeling'''的第三次社会模拟浪潮。与微观模拟一样，这些模型强调自下而上的设计，但采用了与微观模拟不同的四个关键假设: '''自主性 Autonomy'''、'''相互依赖性 Interdependency'''、'''简单规则 Simple Rules'''和'''适应性行为 Adaptive Behavior'''。1981年，数学家、政治学家阿克塞尔罗德（Robert Axelrod）和进化生物学家汉密尔顿（W.D. Hamilton）在《科学》杂志上发表了一篇名为《合作的进化》（The Evolution of Cooperation）的重要论文，该论文采用基于主体建模方法，论证了在一个囚徒困境博弈中，当主体遵循简单的自利规则时，互惠的社会合作是如何建立和稳定的。阿克塞尔罗德和汉密尔顿证明，主体遵循这样一套简单的规则: (1)在第一轮进行合作，(2)在其后重复伙伴以前的行动，能够在没有人口学差异、价值观、宗教和文化等社会规范作为合作的先决条件或中介的情况下制定合作和制裁的“规范”。整个20世纪90年代，像威廉·希姆斯·本布里奇（William Sims Bainbridge），Kathleen Carley，Michael Macy 和 John Skvoretz 这样的学者开发了基于多主体的广义互惠、偏见、社会影响和组织信息处理模型。1999年，吉尔伯特（Nigel Gilbert）出版了第一本关于社会模拟: 写给社会科学家的仿真模拟（Simulation for the social scientist）的教科书，并建立了它最相关的杂志: 人工社会和社会模拟杂志（the Journal of Artificial Societies and Social Simulation）。

===Data mining and social network analysis===

===Data mining and social network analysis 数据挖掘和社会网络分析===

{{main|Data mining|Social network analysis}}

{{main|Data mining|Social network analysis}}

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman. The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments. Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.

Independent from developments in computational models of social systems, social network analysis emerged in the 1970s and 1980s from advances in graph theory, statistics, and studies of social structure as a distinct analytical method and was articulated and employed by sociologists like James S. Coleman, Harrison White, Linton Freeman, J. Clyde Mitchell, Mark Granovetter, Ronald Burt, and Barry Wellman. The increasing pervasiveness of computing and telecommunication technologies throughout the 1980s and 1990s demanded analytical techniques, such as network analysis and multilevel modeling, that could scale to increasingly complex and large data sets. The most recent wave of computational sociology, rather than employing simulations, uses network analysis and advanced statistical techniques to analyze large-scale computer databases of electronic proxies for behavioral data. Electronic records such as email and instant message records, hyperlinks on the World Wide Web, mobile phone usage, and discussion on Usenet allow social scientists to directly observe and analyze social behavior at multiple points in time and multiple levels of analysis without the constraints of traditional empirical methods such as interviews, participant observation, or survey instruments. Continued improvements in machine learning algorithms likewise have permitted social scientists and entrepreneurs to use novel techniques to identify latent and meaningful patterns of social interaction and evolution in large electronic datasets.

社会网络分析独立于社会系统计算模型的发展，在20世纪70年代和80年代出现于图论、统计学和社会结构研究的进展中，作为一种独特的分析方法，被社会学家如 James s. Coleman，Harrison White，Linton Freeman，j. Clyde Mitchell，马克·格兰诺维特，Ronald Burt 和 Barry Wellman 阐述和采用。在整个1980年代和1990年代，计算和通信技术日益普及，要求采用诸如网络分析和多级建模等分析技术，这些技术可以扩展到日益复杂和庞大的数据集。最近的一波计算社会学没有使用模拟，而是使用网络分析和先进的统计技术来分析大规模电子代理数据库中的行为数据。电子记录，如电子邮件和即时消息记录，万维网上的超链接，移动电话的使用，以及 Usenet 上的讨论，使社会科学家能够直接观察和分析社会行为在多个时间点和多个层次的分析，而不受传统的实证方法，如访谈，参与观察，或调查工具的限制。机器学习算法的不断改进同样使得社会科学家和企业家能够使用新技术来识别大型电子数据集中潜在的和有意义的社会互动和进化模式。

'''社会网络分析 Social Network Analysis'''独立于社会系统计算模型的发展，在20世纪70年代和80年代出现于图论、统计学和社会结构的研究中，它作为一种独特的分析方法被社会学家如 James s. Coleman，Harrison White，Linton Freeman，J. Clyde Mitchell，Mark Granovetter，Ronald Burt 和 Barry Wellman 等阐述和采用。在整个1980年代和1990年代，计算和通信技术日益普及，这要求采用诸如网络分析和多级建模等分析技术，这些技术可以扩展到日益复杂和庞大的数据集中。最近的计算社会学没有使用模拟，而是使用网络分析和先进的统计技术来分析大规模计算机数据库中电子代理的行为数据。电子记录，如电子邮件和即时消息记录，万维网上的超链接，移动电话数据，以及 Usenet 上的讨论，使社会科学家能够直接观察社会行为并在多个时间点和多个层次的分析行为，并且不受传统的实证方法，如访谈、观察（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 为了通顺，删除研究对象/被试：participants）或调查工具的限制。机器学习算法的不断改进同样使得社会科学家和企业家能够使用新技术来识别大型电子数据集中潜在但有意义的社会互动和演化模式。

Narrative network of US Elections 2012

Narrative network of US Elections 2012

The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale,  

The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale,  

The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale,  

The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale,  

turning textual data into network data.  The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.<ref>{{cite journal|title=Network analysis of narrative content in large corpora|author1=S Sudhahar|author2=G De Fazio|author3=R Franzosi|author4=N Cristianini|journal=Natural Language Engineering|volume=21|issue=1|pages=1–32|year=2013|doi=10.1017/S1351324913000247 |url=https://research-information.bristol.ac.uk/files/129621186/Network_Analysis_of_Narrative_Content_in_Large_Corpora.pdf}}</ref> This automates the approach introduced by quantitative narrative analysis,<ref>{{cite book|title=Quantitative Narrative Analysis|last=Franzosi|first=Roberto|publisher=Emory University|year=2010}}</ref> whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.<ref name="ReferenceA"/>

turning textual data into network data.  The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.<ref>{{cite journal|title=Network analysis of narrative content in large corpora|author1=S Sudhahar|author2=G De Fazio|author3=R Franzosi|author4=N Cristianini|journal=Natural Language Engineering|volume=21|issue=1|pages=1–32|year=2013|doi=10.1017/S1351324913000247 |url=https://research-information.bristol.ac.uk/files/129621186/Network_Analysis_of_Narrative_Content_in_Large_Corpora.pdf}}</ref> This automates the approach introduced by quantitative narrative analysis,<ref>{{cite book|title=Quantitative Narrative Analysis|last=Franzosi|first=Roberto|publisher=Emory University|year=2010}}</ref> whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.<ref name="ReferenceA"/>

turning textual data into network data.  The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes. This automates the approach introduced by quantitative narrative analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.

turning textual data into network data.  The resulting networks, which can contain thousands of nodes, are then analysed by using tools from Network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes. This automates the approach introduced by quantitative narrative analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.

将文本数据转换为网络数据。由此产生的网络可以包含数千个节点，然后利用网络理论中的工具对其进行分析，以确定关键参与者、关键群体或党派，以及总体网络的健壮性或结构稳定性或某些节点的中心性等一般性质。这使定量叙事分析引入的方法自动化，据此，主语-动词-宾语三元组被认定为由动作连接的成对行为者，或者由行为者-宾语形成的成对行为者。

将文本数据转换为网络数据（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 和前面一段是连起来的吗？）。由此产生的网络可以包含数千个'''节点 Nodes'''，然后利用网络理论中的工具对其进行分析，以确定关键参与者、关键群体，以及网络的总体特性如稳健性、结构稳定性，或某些节点的'''中心性 Centrality'''等。这使'''定量叙事分析Quantitative Narrative Analysis'''引入的方法得以自动化，据此，主语-动词-宾语三元组被看作由动作连接的成对行为者，或者由行为者-宾语形成的成对行为者。

 

 

===Computational content analysis===

===Computational content analysis===