How metrics matter: Mapping reactions to social media metrics in the public issues of Covid-19 misinformation and users' health and well-being

点赞数量或参与率等社交媒体指标已经成为我们当代数据化社会的一个关键问题(Gerlitz&Helmond，2013；Marres，2017)。内容创作者、阴谋和极右翼团体、艺术家和活动人士在截然不同的议程下，正在展开不同的策略，以游戏衡量标准，并干预社交媒体平台上某些内容和账户的可见度、声誉和价值(在)(Brunton&Nissenbaum，2015；Bucher，2018；Cotter，2018；Marwick&Lewis，2017；van der Nagel，2018)。这些策略表明，社交媒体中普遍存在各种形式的“反应性”，即“测量会引起干预其测量对象的人的反应”的想法(Espland和Sauder，2007，第2页)。社交媒体上的反应通常被定义为“不真实的行为”、“偏见的来源”或“欺诈”。出于这个原因，多个平台已经制定了法律政策、算法系统和重新设计的界面，以打击“虚假订婚”并控制反应性。然而，被动实践并不总是意想不到的，也可能是平台刻意设计的成就(Gerlitz&Lury，2014)。这表明，在某些情况下，反应性不仅被接受，而且是社交媒体指标设计中内置的一种效果，但在另一些情况下，它变得有问题，并被描述为对维持健康的社交媒体环境“滥用”或“有害”。这一博士研究项目将把对反应性的既定理解转移到社交媒体指标，将其视为恶意操纵和方法偏差的必然有问题的形式，以便调查它们是如何从一开始就被认定为有问题的。借鉴数字社会学、科学技术研究和量化社会学等相关领域的工作，我的目的是探讨社交媒体中从反应性到指标的独特问题。这个项目随后询问了以下问题：我们与社交媒体中的指标的关系如何、为谁以及为什么结束成为一个令人关注的问题？我将在两个实质性领域探讨这个问题：a)新冠肺炎错误信息的放大，或者衡量指标的反应性如何有助于阴谋论和反疫苗团体的影响；b)社交媒体指标对用户健康和福祉的影响，以及减轻这些影响的当代实验。我将进行多地点数字人种学(Hine，2015)，其中包括使用数字方法进行跨平台分析(Marres，2015；Rogers，2013)，以绘制两个选定的公共问题以及为解决社交媒体中的反应性而提出的证据、示范和干预的形式；(B)参与者观察和对参与社交媒体反应性实践的内容创作者的半结构化访谈，以修改他们的算法可见性，以及与致力于检测和防止指标操纵的工程师和设计师进行的；(C)审查关于公共媒体、非政府组织报告、营销机构手册和政策文献等有关社交媒体指标的实验和争议的二手材料；(D)开发原型或数字探测器，以更具投机性和实践性的调查方式，在研究本身内引起对社交媒体指标的反应。这项研究项目将通过在社交媒体数据研究中引入关于测量的经典社会学辩论，为数字媒体研究和计算社会科学做出贡献。它建议将反应性做法重新思考为体现社交媒体数据“固有的人工”质量的情况(Marres，2017；Marres&Gertliz，2018)，这些数据出现在测量设备和被测量对象之间的生动纠缠中。

Social media metrics such as like counts or engagement rates have become a critical issue in our contemporary datafied societies (Gerlitz & Helmond, 2013; Marres, 2017). Content creators, conspiracy and far-right groups, artists and activists, under contrasting agendas, are unfolding diverse tactics to game the metrics and intervene in the (in)visibility, reputation and value of certain contents and accounts on social media platforms (Brunton & Nissenbaum, 2015; Bucher, 2018; Cotter, 2018; Marwick & Lewis, 2017; van der Nagel, 2018). These tactics indicate the ubiquity in social media of forms of 'reactivity' or the idea that 'measures elicit responses from people who intervene in the objects they measure' (Espeland and Sauder, 2007, p. 2). Reactivity in social media has been commonly framed as 'inauthentic behaviour', 'sources of bias', or 'fraud'. For this reason, multiple platforms have developed legal policies, algorithmic systems and interfaces redesigns to combat 'fake engagement' and control reactivity. However, reactive practices are not always unexpected, but can also be a deliberately designed achievement by platforms (Gerlitz & Lury, 2014). This suggests that, in certain situations, reactivity is not only accepted, but is an effect that is built into the design of social media metrics, but in others, it becomes problematic and characterized as "abusive" or "harmful" to the maintenance of a healthy social media environment.This doctoral research project will move established understandings of reactivity to social media metrics as necessarily problematic forms of malicious manipulation and methodological bias, in order to investigate how they come to be qualified as problematic in the first place. Drawing upon the work of interrelated fields of Digital Sociology, Science and Technology Studies and Sociology of Quantification, my aim is to inquiry into the distinctive problems that emerge from reactivity to metrics in social media. This project interrogates then the following: how, for whom, and for what ends our relations with metrics in social media becomes a matter of concern? I will explore this question in two substantive areas: a) the amplification of Covid-19 misinformation or how metric reactivity has contributed to the reach of conspiracy and anti-vaccine groups, b) the effects of social media metrics on health and well-being of users and contemporary experiments to mitigate these effects. I will conduct a multi-sited digital ethnography (Hine, 2015) that includes a cross-platform analysis with digital methods (Marres, 2015; Rogers, 2013) to map the two selected public issues and the forms of evidence, demonstration, and intervention put forward to address reactivity in social media; (b) participant observation and semi-structured interviews with content creators involved in reactive practices in social media to modify their algorithmic visibility, as well as with engineers and designers dedicated to the detection and prevention of metrics manipulation; (c) a review of secondary materials about experiments and controversies around social media metrics such as public media, NGO reports, marketing agencies brochures and policy literature; (d) the development of prototypes or digital probes to elicit reactions to social media metrics within the research itself, in a more speculative and practice-based line of inquiry. This research project will contribute to Digital Media Studies and Computational Social Science by introducing classical sociological debates about measurement in the study of social media data. It proposes to rethink reactive practices as situations that manifest the "natively artificial" quality of social media data (Marres, 2017; Marres & Gertliz, 2018) that emerge from the lively entanglements between measuring devices and measured objects.