Collaborative Research: A New Infrastructure for Monitoring Social Class Networks

合作研究：监控社会阶层网络的新基础设施

• 批准号: 1357442
• 负责人: Michael Macy
• 金额: $ 19.61万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357442&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Infrastructure; Monitoring

SES-1357488David GruskyStanford UniversitySES-1357442Michael MacyCornell UniversityOver the last 15 years, an ever larger and more diverse population is choosing to interact using social media that record the digital traces of their communications, a development that opens up unprecedented opportunities to study the network foundation of social class relations. Although there is a long tradition of research examining whether social classes in the United States are well-formed, it has been based exclusively on survey and Census data and, by necessity, has ignored the network foundations of class structure and formation. This research takes advantage of the rising amount of interaction with social media to examine that network structure at population scale. The resulting methods will provide the basis for a new and novel research infrastructure for investigating inter-personal interaction within and between social classes in the United States.By using data from a complete crawl of U.S. Twitter users, it becomes possible to measure class barriers to interpersonal interaction. The centerpiece of this approach is the development of methods to measure the class situation of users with profile data, lexical analysis of message content, and housing valuations for geo-located users. To supplement and validate these behavioral measures, a survey will be administered to a random sample of network edges. A similar analysis of Facebook users will be carried out. The resulting data will be used to complete the first network-based analyses of the extent and patterning of the U.S. class structure. In conventional ?static analyses? of the class structure, the size of inter-class differences in behaviors and attitudes (e.g., childrearing practices, political attitudes) is emphasized, while the patterning of inter-class contact and networks that link classes together is ignored. The key question, therefore, is whether the proposed network analyses of class yield a different portrait of the structure of social classes than the static analyses that have dominated decades of class research in the U.S. At the same time, some network-based analyses of class have been attempted in the past, analyses that have relied on an idiosyncratic range of network behaviors that may be discerned with survey methods (especially, assortative mating where people marry persons with similar education and occupational characteristics, and intergenerational social mobility). The analyses undertaken here will reveal whether social media reduces class barriers to interaction relative to the level of class homophily (the tendency of people to associate with similar people) revealed in face-to-face networks available in survey data. These analyses will provide the foundation of a new network-based analysis of class structure.Broader ImpactsIf class barriers are comparatively weak in on-line interactions, standard measurements of class structure will provide an increasingly misleading portrait of civil society and its inclusiveness. It is also plausible, however, that the powerful search algorithms of online platforms allow people to efficiently cull for alters who are similar to themselves. If the latter proves to be the case, it means that the rise of new social media are, contrary to the conventional view, increasing class homophily and polarizing class relations. The research also has a methodological payoff. Because a network-based analysis of social class structure requires high-quality measurements of the class situation of media users, much of the research will focus on developing the methods that make such measurement possible. The social class of users and alters will be imputed by (a) linking geo-located users to their neighborhoods and housing values, (b) exploiting available profile data, (c) carrying out a lexical analysis of message content, and (d) administering surveys to users. These methods, which may be extended to carry out analogous imputations of race, gender, and other ascribed traits, will be of use to researchers in the social sciences, computer science, information science, and other disciplines facing the stock situation in which direct information on individual traits is scarce. The project will also provide new research opportunities for graduates and undergraduates at Cornell University and Stanford University.

在过去的15年里，越来越多的人选择使用社交媒体进行互动，记录他们通信的数字痕迹，这一发展为研究社会阶级关系的网络基础开辟了前所未有的机会。 虽然有一个悠久的研究传统，研究美国的社会阶层是否形成良好，它一直完全基于调查和人口普查数据，并不可避免地忽略了阶级结构和形成的网络基础。这项研究利用了与社交媒体互动量的增加来研究人口规模的网络结构。由此产生的方法将提供一个新的和新颖的研究基础设施，调查在美国社会阶层之间的人际交往，通过使用数据从一个完整的爬行美国Twitter用户，它成为可能来衡量类障碍的人际交往。这种方法的核心是开发方法来衡量用户的类情况与配置文件数据，词法分析的消息内容，和住房估价的地理定位用户。为了补充和验证这些行为测量，将对网络边缘的随机样本进行调查。对Facebook用户也将进行类似的分析。由此产生的数据将用于完成对美国阶级结构的范围和模式的首次基于网络的分析。传统的？静态分析？类结构，行为和态度的类间差异的大小（例如，教育实践、政治态度），而忽视了将各阶层联系在一起的阶层间联系和网络模式。因此，关键的问题是，所提出的阶级网络分析是否产生了与美国几十年来主导阶级研究的静态分析不同的社会阶级结构的肖像。与此同时，过去也尝试过一些基于网络的阶级分析，分析依赖于一系列特殊的网络行为，这些行为可以通过调查方法来识别（特别是，人们与具有相似教育和职业特征的人结婚的替代性交配，以及代际社会流动）。这里进行的分析将揭示社交媒体是否减少了与调查数据中面对面网络中显示的类同质性（人们与相似的人联系的倾向）水平相关的互动障碍。这些分析将提供一个新的基于网络的分析的阶级结构的基础。更广泛的影响如果阶级的障碍是比较弱的在线互动，阶级结构的标准测量将提供一个越来越误导的公民社会及其包容性的画像。然而，在线平台强大的搜索算法允许人们有效地挑选与自己相似的改变，这也是合理的。如果后者被证明是这样的话，这意味着新社交媒体的兴起与传统观点相反，增加了阶级的同质性和两极化的阶级关系。 这项研究也有方法上的好处。由于基于网络的社会阶层结构分析需要高质量的测量媒体用户的阶级状况，大部分的研究将集中在开发的方法，使这种测量成为可能。用户和改变者的社会等级将通过以下方式估算：（a）将地理定位的用户与他们的社区和住房价值联系起来，（B）利用可用的个人资料数据，（c）对消息内容进行词汇分析，以及（d）对用户进行调查。这些方法可以扩展到对种族、性别和其他归因的特征进行类似的归因，对于社会科学、计算机科学、信息科学和其他学科的研究人员来说，这些方法将是有用的，因为在这些学科中，关于个体特征的直接信息是稀缺的。该项目还将为康奈尔大学和斯坦福大学的毕业生和本科生提供新的研究机会。