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RAPID: Social un-distancing: Understanding self-privacy violations in online communities during the Coronavirus pandemic

RAPID：社交疏远：了解冠状病毒大流行期间在线社区中的自我隐私侵犯行为

基本信息

批准号：
2027757
负责人：
Sarah Rajtmajer
金额：
$ 20万
依托单位：
Pennsylvania State Univ University Park
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：
2020-05-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027757&HistoricalAwards=false
关键词：
RAPID Social un distancing Understanding

项目摘要

The Covid-19 global crisis is unprecedented in a number of ways, one being the scale and scope of human interaction through social media, as people across the world have resorted to online mediums to connect with others. Early evidence indicates that this expanded breadth and depth of online activity may magnify privacy risks for individual users, offering increased opportunity for privacy violations. However, aside from some notable exceptions such as contact tracing apps, online connectedness has not been studied through the lens of privacy risk. This project will investigate how increased disclosure of personal information during the Coronavirus crisis poses unique risks to users’ wellbeing, leaving them vulnerable to privacy violations and subsequent harms that can further worsen the current global health crisis. Investigators will develop and distribute anonymized, annotated COVID-19 related datasets collected from online social platforms in the USA and Italy for the purposes of understanding unique risks to individual privacy posed by COVID-19 crisis. Framing self-disclosure as a strategic and inherently social behavior, investigators will study observed individual and collective rewards for sharing and explore how individual cost/benefit calculations are mediated during the Coronavirus crisis. Outcomes of this project will provide insights into the unique evolution of privacy attitudes during crisis, specifically, how oversharing of personal information is expedited or even encouraged, leaving users vulnerable to privacy breaches and exploits. Project outcomes will provide novel computational methods to identify utterances of self-privacy violations and, critically, to contextualize this risky behavior. These insights will be critical for effectively managing the health and well-being of individuals and communities during COVID-19 and future pandemics.The project will develop convolutional neural networks for labeling of emotional and informational textual utterances of self-disclosure on conversational datasets related to Covid-19 crisis. Semantic labeling approaches, to better capture the language of personal information sharing will also be included in the analysis, for a better modeling effort. These methods will be used to furnish fine-grained labels of instances of self-disclosure in user-centric conversations collected from online social platform. In parallel, the investigators will develop game-theoretic models of self-disclosure in social context. These strategic models will support formal understanding of privacy risk vs. social reward at the individual and collective scales. Data collection, algorithm development and model refinement will move forward in tandem, enabling the most rapid possible response during the Coronavirus crisis. Parallel to a focus on domestic users and English-language text, the investigators will collect and analyze data from Italian social and mainstream media in order to explore the cultural and infrastructural “signatures” of these phenomena, as well as to understand self-disclosure at differing points in the epidemic lifecycle. The dataset with annotations as well as open source code related to the models will be shared with the research community.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.

新冠肺炎全球危机在许多方面都是前所未有的，其中之一是通过社交媒体进行人际互动的规模和范围，因为世界各地的人们都利用在线媒体与他人联系。早期的证据表明，在线活动的广度和深度的扩大可能会增加个人用户的隐私风险，为侵犯隐私提供更多的机会。然而，除了一些引人注目的例外，如联系人追踪应用程序，在线连接还没有从隐私风险的角度进行研究。该项目将调查冠状病毒危机期间个人信息披露的增加如何对用户的健康构成独特的风险，使他们容易受到隐私侵犯和随后的伤害，这些伤害可能进一步加剧当前的全球健康危机。调查人员将开发和分发从美国和意大利的在线社交平台收集的匿名、带注释的COVID-19相关数据集，以了解COVID-19危机对个人隐私构成的独特风险。研究人员将自我披露视为一种战略性和固有的社会行为，他们将研究观察到的个人和集体分享的回报，并探索在冠状病毒危机期间如何调节个人成本/收益计算。该项目的成果将深入了解危机期间隐私态度的独特演变，特别是如何加速甚至鼓励个人信息的过度共享，使用户容易受到隐私泄露和利用。项目成果将提供新的计算方法来识别侵犯自我隐私的话语，更重要的是，将这种危险行为置于背景中。这些见解对于在2019冠状病毒病和未来大流行期间有效管理个人和社区的健康和福祉至关重要。该项目将开发卷积神经网络，用于标记与Covid-19危机相关的会话数据集中自我披露的情感和信息文本话语。语义标记方法，以更好地捕获个人信息共享的语言，也将包括在分析中，以更好地建模。这些方法将用于提供从在线社交平台收集的以用户为中心的对话中自我披露实例的细粒度标签。同时，研究者将发展社会情境下自我表露的博弈论模型。这些战略模型将支持在个人和集体尺度上对隐私风险与社会回报的正式理解。数据收集、算法开发和模型改进将同步推进，以便在冠状病毒危机期间做出最迅速的反应。在关注国内用户和英文文本的同时，调查人员将收集和分析意大利社会和主流媒体的数据，以探索这些现象的文化和基础设施“特征”，并了解疫情生命周期不同阶段的自我披露。带有注释的数据集以及与模型相关的开源代码将与研究社区共享。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。