Doctoral Dissertation Research: Examining Social Clustering and Division via Patterns of Purchasing and Reviewing

美国在各种话题上的意见日益两极分化，引起了公众话语和学术界的关注。个性化推荐系统的广泛采用改变了意见的形成、表达、改变和传播的方式。因此，暴露在文化和意识形态群体中往往会强化观点，即使这些利基群体中的个人没有明确地被社会关系联系在一起。该项目将使用来自亚马逊书评和购买的数据来构建一个图书联合购买和联合评审员网络，用来研究意见聚类和选项的两极分化。将特别关注四个关键问题：是否存在密集的图书集群，这些集群之间几乎没有联系？哪些类型的图书会产生集群性？有没有哪种书籍可以在这些集群之间架起桥梁，然后这些书籍是否会聚集到与横切文化维度相对应的较少数量的主题中？考虑到更广阔的市场，架在集群之间的图书的销量更高，而在每个集群中偏离核心的图书销量更少，还是销量相似，但标准差更高？拟议的项目将使用先进的计算方法来收集和处理亚马逊网站上的图书联合采购数据和带有时间戳的书评。图书将被手工编码成类别，并将使用网络分析来确定这些主题在联合采购和联合审查中的结构位置。本文对意见动力学的研究有三个主要贡献：1)更好地理解在线推荐和评论系统是如何反映和促进意见分化的；2)将同质性的理论和模型扩展到人和文化对象的两方网络中的聚集性；3)发展了利用消费者购买和评论的数字痕迹来研究意见动力学和意见极化的新的研究方法。这项研究对理解和解决美国的社会分化问题具有实际意义。它为两极分化的原因和动态提供了一个独特但互补的视角，并可能为弥合分歧的方法提出切实的建议。该项目也是最早利用大数据研究两极分化的项目之一，将为研究界提供新的数据集和工具。此外，这一结果可能会对想要将自己的书瞄准更多样化的受众的作者有所帮助。该项目还将推进对参与该项目的本科生和研究生的培训。

The growing polarization of opinions in America on a wide range of topics has attracted attention in both public discourse and academia. Widespread adoption of personalized recommender systems has transformed the way that opinions are formed, expressed, changed and disseminated. As a consequence, opinions tend to be reinforced by exposure to cultural and ideological grouping even when individuals within those niches are not explicitly tied by social relations. This project will use data from Amazon book reviews and purchases to construct a book co-purchase and co-reviewer network with which to study opinion clustering and polarization of options. Particular attention will be directed to four key questions: Are there dense clusters of books, with few ties between the clusters? What genres of books produce clustering? Are there sorts of books that bridge between the clusters, and do these books then cluster into a smaller number of topics that correspond to crosscutting cultural dimensions? Do books that bridge between clusters enjoy greater sales, given the broader market, fewer sales, given their deviation from the core in each cluster, or similar sales but with a higher standard deviation? The proposed project will make use of advanced computational methods to collect and process book co-purchasing data and time-stamped book reviews from Amazon.com. Books will be hand-coded into categories and network analysis will be used to locate the structural positions of those topics in the co-purchase and co-review. This dissertation makes three principal contributions to research on opinion dynamics: 1) a better understanding of how online recommender and review systems both reflect and promote opinion polarization, 2) the extension of theories and models of homophily to clustering within bipartite networks of people and cultural objects; 3) the development of novel research methods that use digital traces of consumer purchases and reviews to study opinion dynamics and polarization. This research has practical implications for understanding and addressing social divisions in America. It provides a unique but complementary perspective on the causes and dynamics of polarization and may yield practical suggestions for ways to bridge divisions. This project is also one of the first to use "big data" to study polarization, and will provide the research community with new datasets and tools. In addition, the results may be helpful for authors who want to target their books to a more diverse audience. The project will also advance the training of undergraduate and graduate students who work on the project.