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

CHS：小型：社会运动的系统比较和历史分析框架

• 批准号: 1815875
• 负责人: Ceren Budak
• 金额: $ 49.59万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815875&HistoricalAwards=false
• 关键词: CHS; Small; Systematic; Comparative; Historical

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.

该项目将开发研究在线社会运动的方法，这些运动的影响力越来越大。 尽管前景广阔，但使用社交媒体数据的研究仍存在许多缺点，包括缺乏代表性、不完整性以及难以识别相关结果变量。这些缺点已被广泛承认，但其解决方案却难以捉摸。 该项目在数据收集和数据标记步骤中对这些问题做出了智力贡献。 该项目将提高我们对在线运动如何形成、它们如何随时间演变、参与者如何协调以及运动如何改变参与者和更广泛公众的理解。 这项研究将向社区组织者和公民通报在公民参与中产生理想结果的策略、旨在支持此类公民参与的在线平台的设计者，了解哪些设计选择会导致何种类型的公民参与，以及旨在提供准确信息的记者在线社会运动的叙述。该项目还将产生教育影响，因为这些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；并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。在数据收集步骤中，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估；制定基于拓扑和基于内容的数据收集策略；以及开发在给定一组可能策略的情况下执行有效迭代数据收集的方法，通过建模来最大化通过在学习问题系统中做出的一系列选择所获得的预期奖励。在数据标记步骤中，该项目将贡献跨领域文本分类方法的开发，通过众包平台执行具有成本效益的数据标记，以及关于跨流行社交媒体平台迁移学习潜力的新见解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Higher Ground? How Groundtruth Labeling Impacts Our Understanding of Fake News About the 2016 U.S. Presidential Nominees

• DOI: 10.2139/ssrn.3340173
• 发表时间: 2019-01-01
• 期刊: SSRN Journal
• 作者: Bozarth, L;Saraf, A;Budak, C
• 通讯作者: Budak, C

Keyword expansion techniques for mining social movement data on social media

• DOI: 10.1140/epjds/s13688-022-00343-9
• 发表时间: 2022-05-21
• 期刊: EPJ DATA SCIENCE
• 影响因子: 3.6
• 作者: Bozarth，Lia;Budak，Ceren
• 通讯作者: Budak，Ceren

More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks

不仅仅是满足平局：审视人际关系在社交网络中的作用

• 发表时间: 2021
• 期刊: Proceedings of the International AAAI Conference on Weblogs and Social Media
• 作者: Choi, Minje;Budak, Ceren;Romero, Daniel;Jurgens, David
• 通讯作者: Jurgens, David

Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social Media

连接国家：量化多语言者在社交媒体交流中的作用

• DOI: 10.1609/icwsm.v17i1.22174
• 发表时间: 2023
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Mendelsohn, Julia;Ghosh, Sayan;Jurgens, David;Budak, Ceren
• 通讯作者: Budak, Ceren

Event-Driven Analysis of Crowd Dynamics in the Black Lives Matter Online Social Movement

“黑人生命也是命”在线社会运动中人群动态的事件驱动分析

• DOI: 10.1145/3308558.3313673
• 发表时间: 2019
• 期刊: Event-Driven Analysis of Crowd Dynamics in the Black Lives Matter Online Social Movement
• 作者: Peng, Hao;Budak, Ceren;Romero, Daniel M.
• 通讯作者: Romero, Daniel M.