CHS: Small: Systematic Comparative and Historical Analysis Framework for Social Movements

这个项目将开发研究在线社会运动的方法，这些运动正在发挥越来越大的影响。尽管前景看好，但使用社交媒体数据的研究存在一些缺点，包括缺乏代表性、不完整，以及难以确定相关的结果变量。这些缺点得到了广泛的承认，但他们的解决方案被证明是难以捉摸的。该项目在数据收集和数据标记两个步骤中对这些问题作出了智力贡献。这个项目将提高我们对在线运动是如何形成的，它们如何随着时间的推移而演变，参与者如何协调，以及运动如何改变参与者和更广泛的公众的理解。这项研究将告知社区组织者和公民在公民参与中产生理想结果的战略，旨在支持这种公民参与的在线平台的设计者关于哪些设计选择导致何种类型的公民参与，以及旨在为在线社会运动提供准确叙事的记者。该项目还将产生教育影响，因为数据集将用于数据科学课程，并将与更广泛的研究社区共享。本研究的主要目标是提出优化数据收集策略的方法；提出优化数据标签的方法；分析不同数据收集和标签方案下不同规模的社会运动的发展、协调和成功；以及使用精选数据对抗议参与和运动成功的决定因素进行建模。在数据收集阶段，该项目将有助于：对相关工作中使用的社交媒体数据收集方法进行系统评价；制定基于拓扑和基于内容的数据收集战略；制定一套可行的有效迭代数据收集方法，通过建模，最大限度地提高通过在学习问题系统中作出一系列选择而获得的预期回报。在数据标记阶段，该项目将有助于开发跨域文本分类方法，以通过众包平台执行成本效益的数据标记，以及对跨流行社交媒体平台迁移学习的潜力的新见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop methods for studying online social movements that are playing increasingly influential roles. Despite its promise, research using social media data has suffered from a number of shortcomings including lack of representativeness, incompleteness, and the difficulty of identifying relevant outcome variables. Such shortcomings are widely acknowledged, yet their solutions have proven elusive. This project makes intellectual contributions to these issues at both the data collection and data labeling steps. this project will improve our understanding of how online movements are formed, how they evolve over time, how the participants coordinate, and how movements change their participants and the broader public. This study will inform community organizers and citizens of strategies that result in desirable outcomes in civic participation, the designers of online platforms that aim to support such civic engagement about which design choices lead to what type of civic engagement, and journalists that aim to provide accurate narratives of online social movements. The project will also have educational impact, as the datasets will be used in data science courses and will be shared with the broader research community. The main goals of this research are to propose methods for optimizing data collection strategies; present methods for optimizing data labeling; analyze the development, coordination, and success for social movements of varying scales under different data collection and labeling schemes; and model determinants of protest participation and movement success using curated data. At the data collection step, the project will contribute: a systematic evaluation of the social media data collection methods used in related work; the formulation of topology-based and content-based data collection strategies; and the development of methods to perform effective iterative data collection given a set of possible strategies, through modeling to maximize the expected reward earned through a sequence of choices made in a system of learning problems. At the data labeling step, the project will contribute the development of cross-domain text classification methods to perform cost-effective data labeling through crowdsourcing platforms, and new insights about the potential for transfer learning across popular social media platforms.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.