III: RAPID: Stay-at-home attitudes and their impact on the COVID-19 pandemic

III：RAPID：呆在家里的态度及其对 COVID-19 大流行的影响

• 批准号: 2031095
• 负责人: Elena Zheleva
• 金额: $ 9.93万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031095&HistoricalAwards=false
• 关键词: III; RAPID; Stay; home; attitudes

The rapid spread of the novel SARS-CoV-2 coronavirus has paralyzed societies and strained health care systems, with a rising death toll and severe economic consequences. Stay-at-home orders around the globe have been embraced by some and protested by others. At the same time, little is known about the spectrum of attitudes towards these orders and people's justifications for following or resisting them. This research will develop algorithms for analyzing stay-at-home attitudes on social media, connecting these attitudes to pandemic impact through a novel visual representation that takes geographical location and socioeconomic context into account. This research will bring greater awareness to the public about the role of values that protect life in public discourse and their influence on citizens' appraisal of policies that affect their own well-being. Shared values, beliefs, and understandings build the social cohesion and cooperation needed to build greater economic prosperity. The results of this project will help policy makers craft more persuasive public health directives to enhance public health.Framing--highlighting certain aspects of an issue or event--can have a significant impact on the formation of perspective. To address the complexity of modern information networks, this project will develop algorithms that automatically detect frames propagated through social media. It focuses on value frames because people use those values to justify a position and issues can be re-framed accordingly to appeal to and change the opinions of target audiences. Creating the first dataset of its kind, this project will collect and annotate tweets that include stay-at-home attitudes and core value frames. Current state-of-the-art approaches to detecting attitudes in tweets are limited to binary or ternary classification of either sentiment (positive, negative, neutral) or language type (abusive versus "normal"). By bringing together state-of-the-art deep learning models with models that have more explanatory power, this project will devise a novel methodology for identifying values frames in microblogs that takes advantage of semantic and discourse structure information. By enabling statistical computing and analysis over geospatial data, for which few techniques exist currently, this research will make it possible to analyze datasets at multiple levels of spatial aggregation and to compare temporal and spatial differences to enhance participation and promote positive public health outcomes.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.

新型SARS-CoV-2冠状病毒的快速传播使社会瘫痪，医疗保健系统紧张，死亡人数不断增加，经济后果严重。在地球仪上，一些人接受了“呆在家里”的命令，另一些人则提出了抗议。与此同时，人们对这些命令的各种态度以及人们遵守或抵制这些命令的理由知之甚少。这项研究将开发用于分析社交媒体上的居家态度的算法，通过考虑地理位置和社会经济背景的新颖视觉表示将这些态度与流行病影响联系起来。这项研究将使公众更好地认识到保护生命的价值观在公共讨论中的作用及其对公民评价影响其自身福祉的政策的影响。共同的价值观、信仰和理解建立了社会凝聚力和合作，这是实现更大经济繁荣所必需的。这个项目的结果将有助于决策者制定更有说服力的公共卫生指令，以加强公共卫生。框架-突出一个问题或事件的某些方面-可以对观点的形成产生重大影响。为了解决现代信息网络的复杂性，该项目将开发自动检测通过社交媒体传播的帧的算法。它侧重于价值观框架，因为人们使用这些价值观来证明一个立场是正确的，而问题可以相应地重新制定，以吸引和改变目标受众的意见。该项目创建了第一个此类数据集，将收集和注释包括呆在家里的态度和核心价值框架的推文。目前检测推文中态度的最先进方法仅限于情感（积极、消极、中性）或语言类型（辱骂与“正常”）的二元或三元分类。通过将最先进的深度学习模型与具有更强解释力的模型结合起来，该项目将设计一种新的方法来识别微博中的价值框架，该方法利用语义和话语结构信息。通过对地理空间数据进行统计计算和分析，目前这方面的技术很少，这项研究将使分析多个空间聚集层次的数据集成为可能，并比较时间和空间差异，以提高参与度和促进积极的公共卫生成果。该奖项反映了NSF的法定使命，并被认为值得通过利用基金会的智力价值进行评估来支持和更广泛的影响审查标准。

项目成果

Detecting and understanding moral biases in news

• DOI: 10.18653/v1/2020.nuse-1.15
• 发表时间: 2020-07
• 作者: Usman Shahid;Barbara Maria Di Eugenio;Andrew Rojecki;E. Zheleva

Understanding Stay-at-home Attitudes through Framing Analysis of Tweets

• DOI: 10.1109/dsaa54385.2022.10032455
• 发表时间: 2022-09
• 期刊: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)
• 作者: Zahra Fatemi;Abari Bhattacharya;A. Wentzel;Vipul Dhariwal;Lauren E. Levine;Andrew Rojecki;G. Marai-G.-Mara