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

新冠肺炎全球危机在许多方面都是史无前例的，其中之一是通过社交媒体进行人类互动的规模和范围，因为世界各地的人们都借助网络媒体与他人建立联系。早期证据表明，这种扩大的在线活动广度和深度可能会放大个人用户的隐私风险，增加侵犯隐私的机会。然而，除了一些值得注意的例外，如联系人追踪应用程序，在线连接性还没有从隐私风险的角度进行研究。该项目将调查在冠状病毒危机期间个人信息的更多披露如何对用户的福祉构成独特的风险，使他们容易受到隐私侵犯和随后的伤害，这可能会进一步恶化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、带注释的新冠肺炎相关数据集，目的是了解新冠肺炎危机对个人隐私构成的独特风险。将自我表露作为一种战略性和内在的社会行为，调查人员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个人成本/收益计算是如何进行的。该项目的成果将提供对危机期间隐私态度的独特演变的见解，特别是如何加速甚至鼓励过度分享个人信息，使用户容易受到隐私侵犯和利用的影响。项目成果将提供新的计算方法来识别侵犯自我隐私的言论，并至关重要的是，将这种危险行为与背景联系起来。这些见解将对有效管理新冠肺炎期间和未来大流行期间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，用于在与新冠肺炎危机相关的对话数据集上标记自我表露的情感和信息文本话语。语义标签方法，以更好地捕捉个人信息共享的语言也将包括在分析中，以便更好地进行建模工作。这些方法将被用来提供从在线社交平台收集的以用户为中心的对话中自我表露实例的细粒度标签。同时，研究人员将开发社会背景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规模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齐头并进，以便在冠状病毒危机期间尽可能快速地作出反应。在关注国内用户和英语文本的同时，调查人员将收集和分析来自意大利社交媒体和主流媒体的数据，以探索这些现象的文化和基础设施“签名”，并了解在疫情生命周期的不同时间点的自我披露。带有注释的数据集以及与模型相关的开放源代码将与研究社区共享。这一奖项反映了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。

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.