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

美国在广泛议题上日益分化的观点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形成、表达、改变和传播的方式。因此，观点往往会通过接触文化和意识形态群体而得到强化，即使这些群体中的个人并没有明确地受到社会关系的束缚。本项目将使用亚马逊的书评和购买数据构建一个共同购买和共同评论者网络，研究意见聚类和选项极化。特别要注意的是四个关键问题：是否存在密集的书籍集群，集群之间的联系很少？什么类型的书会产生聚类？是否有某种类型的书在这两类丛书之间架起了桥梁，然后这些书是否会聚集成与横切文化维度相对应的较小数量的主题？在市场更广阔的情况下，在不同丛书之间架起桥梁的图书销量会更高吗？在偏离每个丛书核心的情况下，销量会更低吗？还是销量相似，但标准差更高？拟议中的项目将利用先进的计算方法来收集和处理亚马逊网站上的图书共同购买数据和带有时间戳的书评。书籍将被手工编码成类别，网络分析将被用来定位这些主题在共同购买和共同审查中的结构位置。本文对意见动态的研究做出了三个主要贡献：1)更好地理解在线推荐和评论系统如何反映和促进意见极化；2)将同质性的理论和模型扩展到人与文化对象的二元网络中的聚类；3)利用消费者购买和评论的数字痕迹来研究意见动态和两极分化的新研究方法的发展。这项研究对理解和解决美国的社会分化具有实际意义。它对两极分化的原因和动态提供了独特而又互补的观点，并可能为弥合分歧的方法提出切实可行的建议。该项目也是首批使用“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，这些结果可能对那些希望将自己的书定位于更多样化的读者的作者有所帮助。该项目还将促进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.