EAGER: Identifying Methodological and Ethical Challenges in Online Research of Hard-to-Reach Populations during the COVID-19 pandemic

2019冠状病毒病大流行要求重新调整收集人类行为数据的技术，这对这些数据的可靠性和有效性提出了新的挑战。但它也为数据收集和分析方法的创新提供了机会，特别是在对社交网络的分析方面。该项目试点了一种新的方法技术，询问数字平台在吸引更多弱势群体方面是否具有更高的效率。这项研究的结果将以有利于公共卫生工作的方式传播。这项科学研究还将扩大代表性不足的群体对共同生产科学知识的参与。提出的更广泛的科学问题是，是否可以从边缘化人群中收集有效和可靠的数据。它试点了一种新的数字人种学方法，通过将个人（自我中心）社会网络的社会网络映射与定性数据收集方法（半结构化访谈，重点是通过建立研究参与者与研究团队之间的关系，以及社交媒体和其他在线平台内的观察技术，确保数据有效）相结合，识别边缘化人群中的社会网络。如果社会网络分析（SNA）和数字人种学技术的结合能够可靠地为边缘化/污名化人群提供有效数据，那么数字场所可能被证明是与弱势群体接触的更安全、更有效的空间，这反过来又可能使难以接触的人群的数据快速积累成为可能。本探索性项目旨在通过识别各种边缘群体的网络属性、集群边界和社会空间，为研究隐藏/边缘群体的社会科学研究方法做出方法论贡献。因此，该项目将加强对社会网络理论和方法的智力辩论，并可能通过提供替代研究方法来研究难以接触到的人群，从而成为一种模式。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。

The COVID-19 pandemic has necessitated the retooling of techniques for collecting human behavioral data, in ways that present new challenges to the reliability and validity of that data. But it has also provided an opportunity for innovating methods of data collection and analysis, particularly with respect to the analysis of social networks. This project pilots a new methodological technique, asking whether digital platforms have a higher degree of efficacy in engaging more vulnerable populations. Findings from this research will disseminated in a way that aims to benefit public health efforts. This scientific study will also broaden the participation of the underrepresented groups in the co-production of scientific knowledge. The broader scientific question proposed is whether valid and reliable data can be gathered virtually from a marginalized population. It pilots a new digital ethnographic method for identifying social networks among marginalized populations by coupling the social network mapping of personal (egocentric) social networks with qualitative data collection methods (semi-structured interviews that are focused on ensuring data valid through the building of rapport between research participants and the research team, and observational techniques within social media and other online platforms). If the combination of social network analysis (SNA) and digital ethnographic techniques can reliably yield valid data for marginalized/stigmatized populations, digital venues may prove to be a safer and more efficient space for engaging with vulnerable populations, which in turn may potentially allow for the rapid accumulation of data of hard-to-reach populations. This exploratory project intends to make methodological contributions to social science research methods in regards to studying hidden/marginalized populations through the identification of network properties, cluster boundaries, and the social spaces of various marginalized groups. As a result, this project will enhance the intellectual debates on social network theories and methodology, and may come to serve as a model by providing alternative research methods to study hard-to-reach populations.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.