更改

文本语料库的自动解析使对参与者及其关系网络的大规模提取成为可能，它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 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。

文本语料库的自动解析使对参与者及其关系网络的大规模提取成为可能，它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 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 计算机内容分析 （名词翻译可吗？）===

===Computational content analysis 计算<font color='red'><s>机</s></font>内容分析 （名词翻译可吗？）===

[[Content analysis]] has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "[[big data]]" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. [[Gender bias]], [[readability]], content similarity, reader preferences, and even mood have been analyzed based on [[text mining]] methods over millions of documents.<ref>{{cite journal|author1=I. Flaounas|author2=M. Turchi|author3=O. Ali|author4=N. Fyson|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=The Structure of EU Mediasphere|journal=PLOS One|volume=5|issue=12|pages=e14243|year=2010|doi=10.1371/journal.pone.0014243|url=https://orca-mwe.cf.ac.uk/50732/1/Flaounas%202010.pdf|pmid=21170383|pmc=2999531|bibcode=2010PLoSO...514243F}}</ref><ref>{{cite journal|title=Nowcasting Events from the Social Web with Statistical Learning|author1=V Lampos|author2=N Cristianini|journal=ACM Transactions on Intelligent Systems and Technology |volume=3|issue=4|page=72|doi=10.1145/2337542.2337557|year=2012|url=http://www.lampos.net/sites/default/files/papers/lampos2012nowcasting.pdf}}</ref><ref>{{cite conference|title=NOAM: news outlets analysis and monitoring system|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T Snowsill|author5=F Nicart|author6=T De Bie|author7=N Cristianini|conference=Proc. of the 2011 ACM SIGMOD international conference on Management of data|year=2011|url=http://www.tijldebie.net/system/files/SIGMOD_11_demo_Ilias.pdf|doi=10.1145/1989323.1989474}}</ref><ref>{{cite book|author=N Cristianini|title=''Combinatorial Pattern Matching''|pages=2–13|year=2011|volume=6661|series= Lecture Notes in Computer Science|isbn=978-3-642-21457-8|doi=10.1007/978-3-642-21458-5_2|chapter=Automatic Discovery of Patterns in Media Content|citeseerx=10.1.1.653.9525}}</ref><ref>{{Cite journal|last=Lansdall-Welfare|first=Thomas|last2=Sudhahar|first2=Saatviga|last3=Thompson|first3=James|last4=Lewis|first4=Justin|last5=Team|first5=FindMyPast Newspaper|last6=Cristianini|first6=Nello|date=2017-01-09|title=Content analysis of 150 years of British periodicals|url=http://www.pnas.org/content/early/2017/01/03/1606380114|journal=Proceedings of the National Academy of Sciences|volume=114|issue=4|language=en|pages=E457–E465|doi=10.1073/pnas.1606380114|issn=0027-8424|pmid=28069962|pmc=5278459}}</ref> The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.<ref>{{cite journal|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T. Lansdall-Welfare|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=Research methods in the age of digital journalism|journal=Digital Journalism|year=2012|doi=10.1080/21670811.2012.714928|volume=1|pages=102–116}}</ref> showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.<ref>{{cite conference|title=Effects of the Recession on Public Mood in the UK|author=T Lansdall-Welfare|author2=V Lampos|author3=N Cristianini|series=Mining Social Network Dynamics (MSND) session on Social Media Applications|doi=10.1145/2187980.2188264|conference=Proceedings of the 21st International Conference on World Wide Web|pages=1221–1226|location=New York, NY, USA|url=http://www.cs.bris.ac.uk/Publications/Papers/2001521.pdf}}</ref>

[[Content analysis]] has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "[[big data]]" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. [[Gender bias]], [[readability]], content similarity, reader preferences, and even mood have been analyzed based on [[text mining]] methods over millions of documents.<ref>{{cite journal|author1=I. Flaounas|author2=M. Turchi|author3=O. Ali|author4=N. Fyson|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=The Structure of EU Mediasphere|journal=PLOS One|volume=5|issue=12|pages=e14243|year=2010|doi=10.1371/journal.pone.0014243|url=https://orca-mwe.cf.ac.uk/50732/1/Flaounas%202010.pdf|pmid=21170383|pmc=2999531|bibcode=2010PLoSO...514243F}}</ref><ref>{{cite journal|title=Nowcasting Events from the Social Web with Statistical Learning|author1=V Lampos|author2=N Cristianini|journal=ACM Transactions on Intelligent Systems and Technology |volume=3|issue=4|page=72|doi=10.1145/2337542.2337557|year=2012|url=http://www.lampos.net/sites/default/files/papers/lampos2012nowcasting.pdf}}</ref><ref>{{cite conference|title=NOAM: news outlets analysis and monitoring system|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T Snowsill|author5=F Nicart|author6=T De Bie|author7=N Cristianini|conference=Proc. of the 2011 ACM SIGMOD international conference on Management of data|year=2011|url=http://www.tijldebie.net/system/files/SIGMOD_11_demo_Ilias.pdf|doi=10.1145/1989323.1989474}}</ref><ref>{{cite book|author=N Cristianini|title=''Combinatorial Pattern Matching''|pages=2–13|year=2011|volume=6661|series= Lecture Notes in Computer Science|isbn=978-3-642-21457-8|doi=10.1007/978-3-642-21458-5_2|chapter=Automatic Discovery of Patterns in Media Content|citeseerx=10.1.1.653.9525}}</ref><ref>{{Cite journal|last=Lansdall-Welfare|first=Thomas|last2=Sudhahar|first2=Saatviga|last3=Thompson|first3=James|last4=Lewis|first4=Justin|last5=Team|first5=FindMyPast Newspaper|last6=Cristianini|first6=Nello|date=2017-01-09|title=Content analysis of 150 years of British periodicals|url=http://www.pnas.org/content/early/2017/01/03/1606380114|journal=Proceedings of the National Academy of Sciences|volume=114|issue=4|language=en|pages=E457–E465|doi=10.1073/pnas.1606380114|issn=0027-8424|pmid=28069962|pmc=5278459}}</ref> The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.<ref>{{cite journal|author1=I. Flaounas|author2=O. Ali|author3=M. Turchi|author4=T. Lansdall-Welfare|author5=T. De Bie|author6=N. Mosdell|author7=J. Lewis|author8=N. Cristianini|title=Research methods in the age of digital journalism|journal=Digital Journalism|year=2012|doi=10.1080/21670811.2012.714928|volume=1|pages=102–116}}</ref> showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.<ref>{{cite conference|title=Effects of the Recession on Public Mood in the UK|author=T Lansdall-Welfare|author2=V Lampos|author3=N Cristianini|series=Mining Social Network Dynamics (MSND) session on Social Media Applications|doi=10.1145/2187980.2188264|conference=Proceedings of the 21st International Conference on World Wide Web|pages=1221–1226|location=New York, NY, USA|url=http://www.cs.bris.ac.uk/Publications/Papers/2001521.pdf}}</ref>

Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents. The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al. showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.

Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents. The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al. showing how different topics have different gender biases and levels of readability; the possibility to detect mood shifts in a vast population by analysing Twitter content was demonstrated as well.

长期以来，内容分析一直是社会科学和媒体研究的传统方法。内容分析的自动化使得这一领域发生了一场“大数据”革命，这些研究中，社交媒体和报纸内容包括了数百万条的新闻。基于文本挖掘方法，'''性别偏差Gender Bias'''、'''可读性 Readability'''、'''内容相似度 Content Similarity'''、'''读者偏好 Reader Preferences'''，甚至是'''情绪 Mood'''的数以百万计的文档都可以被分析。Flaounas等人对可读性、性别偏差和'''话题偏差 Topic Bias'''为主要研究对象，展示了不同的话题如何有不同水平的性别偏差和可读性; 展示了通过分析推特内容来检测人群情绪变化的可能性。

长期以来，内容分析一直是社会科学和媒体研究的传统方法。内容分析的自动化使得这一领域发生了一场“大数据”革命，这些研究中，社交媒体和报纸内容包括了数百万条的新闻。基于<font color='blue'>数以百万计的文档和</font>文本挖掘方法，'''性别偏差Gender Bias'''、'''可读性 Readability'''、'''内容相似度 Content Similarity'''、'''读者偏好 Reader Preferences'''，甚至是'''情绪 Mood'''<s>的数以百万计的文档</s>都可以被分析。Flaounas等人对可读性、性别偏差和'''话题偏差 Topic Bias'''<s>为主要研究对象</s><font color='blue'>的研究</font>，展示了不同的话题如何有不同<s>水平</s>的性别偏差和可读性<font color='blue'>水平</font>; <s>展示了</s><font color='blue'>以及</font>通过分析推特内容来检测人群情绪变化的可能性。