Collaborative Research: A New Infrastructure for Monitoring Social Class Networks.

SES-1357488 David Gruski斯坦福大学SES-1357442 Michael MacyCornell大学在过去的15年里，越来越多和更多样化的人口选择使用社交媒体进行互动，这些媒体记录了他们交流的数字痕迹，这一发展为研究社会阶层关系的网络基础打开了前所未有的机会。尽管研究美国的社会阶层是否构成良好有着悠久的传统，但它一直完全基于调查和人口普查数据，不可避免地忽视了阶级结构和形成的网络基础。这项研究利用与社交媒体的互动量不断增加的优势，在人口规模上检验这种网络结构。由此产生的方法将为一种新的、新颖的研究基础设施提供基础，以调查美国社会阶层内部和社会阶层之间的人际互动。通过使用来自美国Twitter用户的完整爬虫数据，测量人际互动的阶层障碍成为可能。这种方法的核心是开发方法，通过配置文件数据、消息内容的词法分析和地理位置用户的住房评估来衡量用户的类别情况。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。将对Facebook用户进行类似的分析。所得数据将用于完成对美国阶级结构的程度和模式的第一次基于网络的分析。在传统的静态分析中？在班级结构中，强调了班级间行为和态度差异的大小(例如，抚养子女的做法、政治态度)，而忽视了班级间联系和将班级联系在一起的网络的模式。因此，关键问题是，与主导美国几十年阶级研究的静态分析相比，拟议的阶级网络分析是否产生了不同于社会阶级结构的图景。同时，过去曾尝试过一些基于网络的阶级分析，这些分析依赖于一系列特殊的网络行为，可以通过调查方法辨别出来(特别是在人们与教育和职业特征相似的人结婚，以及代际社会流动性的情况下)。这里进行的分析将揭示，相对于调查数据中可用的面对面网络中显示的阶层同质性(人们与相似人交往的倾向)水平，社交媒体是否减少了阶层互动的障碍。这些分析将为新的基于网络的阶级结构分析提供基础。广泛的影响如果阶级障碍在在线互动中相对较弱，那么对阶级结构的标准测量将提供对公民社会及其包容性的越来越具误导性的描述。然而，在线平台强大的搜索算法允许人们高效地挑选与自己相似的改变者，这也是有道理的。如果事实证明是后者，这意味着与传统观点相反，新社交媒体的崛起增加了阶级的同质性，并使阶级关系两极分化。这项研究也有方法论上的回报。由于基于网络的社会阶层结构分析需要对媒体用户的阶层状况进行高质量的测量，因此大部分研究将集中在开发使这种测量成为可能的方法上。将通过(A)将地理位置的用户与他们的社区和住房价值联系起来，(B)利用现有的个人资料数据，(C)对消息内容进行词法分析，以及(D)对用户进行调查，来确定用户和更改者的社会阶层。这些方法可以扩展到对种族、性别和其他归因特征进行类似的归因，将对社会科学、计算机科学、信息科学和其他学科的研究人员有用，因为在这种情况下，关于个人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。

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.