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.

SES-1357488david Gruskystanford Universityses-1357442Michael Macycornell University持续了15年，一个越来越大，更多样化的人群选择使用社交媒体进行社交媒体进行交互，这些社交媒体记录其通信的数字痕迹，这一发展使人们开辟了前所未有的社交类别基础网络基础的机会。 尽管研究有一个悠久的研究，研究了美国的社会阶层是否形成良好，但它仅基于调查和人口普查数据，并且必须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的互动量增加，以检查人口规模的网络结构。最终的方法将为研究美国社会阶层和社会阶层之间的人际关系互动提供新的新型研究基础架构。通过使用来自美国Twitter用户的完全爬网的数据，可以衡量人际关系互动的类障碍。这种方法的核心是开发通过配置文件数据，消息内容的词汇分析以及为地理用户的住房估值来衡量用户的阶级状况的开发。为了补充和验证这些行为指标，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。最终的数据将用于完成有关美国类结构范围和模式的第一个基于网络的分析。在常规的静态分析中？在阶级结构中，强调了行为和态度的阶层间差异的规模（例如，育儿实践，政治态度），而阶层间接触的模式和将阶级联系在一起的网络的模式被忽略了。因此，关键的问题是，班级的拟议网络分析是否产生社会阶层结构的肖像是否与在美国几十年来统治了几十年的课堂研究的静态分析，过去已经尝试了一些基于网络的阶级分析，这些分析在过去的分析中，这些分析依赖于某些人的特定范围的人（特别是识别的人）（尤其是识别的人）（尤其是态度）（尤其是态度）。职业特征和代际社会流动性）。这里进行的分析将揭示社交媒体在调查数据中可用的面对面网络中揭示的相对于同质阶层（人们与类似人交往的趋势）相对于班级的互动障碍（人们与类似人交往的趋势）。这些分析将为基于网络结构的新分析提供基础。BroaderImpactsif类障碍在线互动中相对较弱，阶级结构的标准测量将为民间社会及其包容性提供越来越误导的肖像。但是，在线平台的强大搜索算法使人们能够有效地为与自己相似的变化而有效地淘汰。如果后者被证明是这种情况，则意味着新的社交媒体的兴起与传统观点相反，从而增加阶级同质和两极分化的阶级关系。 该研究还具有方法论回报。由于基于网络的社会阶层结构的分析需要对媒体用户的阶级状况进行高质量的测量，因此许多研究将着重于开发使这种测量成为可能的方法。 （a）将地理位置的用户链接到其社区和住房价值，（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。这些方法可能会扩展到对种族，性别和其他归因性状进行类似的归纳，将对社会科学，计算机科学，信息科学以及其他学科的研究人员用于股票状况，在这种情况下，直接信息有关单个特征的直接信息很少。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。