RAPID: Immediate Social Media Data Collection in Key Election Contexts

选举对网络空间的安全性和可信度提出了严峻挑战。选举期间协调一致的信息行动和在线政治暴力组织威胁着众多民主国家的完整性。我们缺乏可比较的跨国数据来帮助我们了解这些威胁如何在不同国家运作，以及政治机构、网络基础设施和社会经济背景如何塑造这些威胁。我们还缺乏有关候选人社交媒体行为的全面数据。该项目将极大地增加学者获取有关选举候选人社交媒体行为以及选民如何应对这种行为的数据的机会。这项工作将能够对竞选期间虚假信息如何在网上传播以及这些信息如何影响面临民主扩张不同阶段的一系列政体的普通选民的信念进行目前不可能的调查。这些数据还可以向有兴趣确保选举安全的政府提供信息，并可以帮助社交媒体公司了解其平台在竞选期间的使用方式。该项目的重点是为学者们建立一个数据资源，这些数据来自围绕两极分化、民主化和其他相对不稳定的背景下即将举行的选举的社交媒体数据收集。研究团队有目的地对候选人社交媒体数据收集的选举背景进行抽样，这些数据符合基于民主、两极分化、稳定性和网络基础设施水平的客观标准。在所有这些背景下，研究团队正在编译社交媒体候选人数据，包括候选人（公共）社交媒体账户、公开的候选人背景特征以及捕捉国家、选举和席位背景的其他变量。在其中三个背景下，研究小组正在选民中进行一项调查，收集以下数据：选民获取候选人在线信息的能力、选民对候选人社交媒体活动的态度、候选人通过数字网络动员线下政治活动（包括选举暴力）的程度、政客利用在线平台传播虚假信息的普遍性和有效程度，以及选民对其背景下政治网络空间安全性和稳定性的看法。研究团队正在以网络研讨会系列的形式为政治学、公共政策和网络安全领域的研究生开展培训，以鼓励使用这些数据进行多学科研究。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。

Elections pose a critical challenge to the security and trustworthiness of cyberspace. Coordinated information operations and online organization of political violence during elections threaten the integrity of a wide range of democratic countries. We lack comparable cross-national data that can help us to understand how these threats operate across countries, and how political institutions, cyber-infrastructure, and socio-economic context shape such threats. We also lack comprehensive data about candidate social media behavior. This project will dramatically increase scholars' access to data on electoral candidates' social media behavior and how voters respond to this behavior. This work will allow currently impossible investigations of how false information travels online during electoral campaigns and how such information affects beliefs of average voters across a range of polities facing different stages of democratic expansion. These data can also inform governments interested in securing their elections and can assist social media firms in understanding how their platforms are used during campaigns.This project is focused on building a data resource for scholars from social media data collection surrounding upcoming elections in polarized, democratizing, and otherwise relatively unstable contexts. The research team is purposively sampling election contexts of candidate social media data collection that meet objective criteria based on their level of democracy, polarization, stability, and cyber infrastructure. Across all of these contexts, the research team is compiling social media candidate data, including candidate (public) social media accounts, publicly available candidate background characteristics, and other variables capturing the national, election, and seat(s) context. In three of these contexts, the research team is executing a survey among voters, collecting data on voters' ability to access candidates' online messages, voters' attitudes towards candidates' social media activity, the extent to which candidates mobilize offline political activity (including election violence) through digital networks, how commonly and effectively politicians spread disinformation using online platforms, and voters' perceptions of the security and stability of political cyberspace in their context. The research team is developing training in the form of webinar series for graduate students in political science, public policy, and cybersecurity to encourage multi-disciplinary research using the data.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.