Modern Approaches for the Analysis of Social Media Data

该研究项目将开发用于分析社交媒体应用产生的高分辨率数据的统计方法。技术的发展使得从社交媒体平台上以前所未有的速度和数量积累数据成为可能。然而，目前用于分析这些数据的方法要么缺乏可解释性，要么需要大量的计算，要么需要严格的数据方案。这个项目将使用一个灵活的建模框架来提取用户行为的相关信息。虽然该项目主要关注Twitter数据，但所开发的方法将适用于其他社交媒体数据，如Facebook、Instagram、Reddit或TikTok，或任何形式的数字互动。这项研究的结果将有助于管理者、政策制定者和利益相关者更好地了解他们在社交媒体上互动的行为者的类型。这个项目创建的方法和代码将公开提供。研究人员将指导本科生和研究生，并与K12学生互动，这些学生对使用有效的统计方法解决在线社交媒体中出现的问题感兴趣。本研究计划将发展二元及分类功能资料结构的统计方法。函数数据的标准分析基本上依赖于内在函数在紧区间上连续的假设。然而，社交媒体数据的复杂性需要不同的假设。Twitter用户的发布模式可以定义为发布活动的某些特征的时间序列。然后可以将用户的数据视为在时域上定义的二值或分类值随机函数，并在精细网格点上观察。该项目将开发非连续功能数据的分类方法，提出计算效率高的估计算法，并研究其理论性质。待开发的方法将被扩展，以适应在纵向设计中获得的每个受试者的多个二值或分类值曲线，以及解释额外的协变量信息。回归模型与分类值功能协变量及其相关的显著性检验也将发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will develop statistical methods for the analysis of high-resolution data arising from social media applications. Technological growth has made possible the accumulation of data from social media platforms at unprecedented speed and volume. However, current methods for analyzing these data either lack interpretability, are computationally intense, or require a rigid data regimen. This project will use a flexible modeling framework to extract relevant information on a user's behavior. Although the project primarily will focus on Twitter data, the methods to be developed will be applicable to other social media data, such as Facebook, Instagram, Reddit, or TikTok, or any form of digital interaction. The results of this research will help managers, policymakers, and stakeholders better understand the types of actors they are interacting with on social media. The methods and code created by this project will be made publicly available. The investigators will mentor undergraduate and graduate students and interact with K12 students who are interested in using valid statistical approaches to solve problems arising in online social media.This research project will develop statistical methods for binary and categorical functional data structures. The standard analysis of functional data relies fundamentally on the assumption that the intrinsic functions are continuous over the compact interval. The complexity of social media data, however, requires different assumptions. A Twitter user's posting pattern can be defined as a time series of some feature of the posting activity. The user's data then can be viewed as a binary-valued or categorical-valued random function defined over a time domain and observed at a fine grid point. The project will develop classification methods for non-continuous functional data, propose computationally efficient estimation algorithms, and study their theoretical properties. The methods to be developed will be extended to adapt to multiple binary-valued or categorical-valued curves per subject, acquired in a longitudinal design, as well as to account for additional covariate information. Regression models with categorical-valued functional covariates and their associated significance tests also will be developed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.