Accounting for the social: Investigating commensuration and Big Data practices at Facebook

Abstract

This study explores Big Data practices at Facebook through an investigation of the role of commensuration or 'the transformation of different qualities into a common metric' in the structuration of analysis and interaction with a major online social media platform. It proposes a conceptual framework and demonstrates the empirical potential of a pragmatic approach based on reading published materials and available documentation. Facebook's Data Warehousing and Analytics Infrastructure serves as an illustrative example to begin tracing out and describe data assemblages in more detail. In being attentive to the motivations, drivers and challenges engineers face when dealing with Big Data, it is argued that their solutions can enable and support but also constrain specific analytical and transactional capabilities or data flows between various devices and actors. The analysis thus moves beyond methodological critiques of the utility of Big Data that lack empirical support and specificity. It is further argued that analytics not just describe but also actively participate in the enactment of social worlds, thereby opening possibilities for new markets or market segments to arise. Online sociality accounts for a model of the social that makes it visible and measurable qua markets inviting data recontextualisation and the creation of value along multiple axes. Contra Facebook's claim to make the web more 'social', an investigation of commensuration brings to the fore the question how the social is accounted for in the first place.

Data journeys: Capturing the socio-material constitution of data objects and flows

“…Whilst various calls have been made for critical engagement with the philosophical and methodological assumptions surrounding 'Big Data' (boyd and Crawford, 2012;Dalton and Thatcher, 2014;Gitelman and Jackson, 2013), relatively few scholars have conducted empirical work on specific 'Big Data' practices. Amongst those that have, many have remained external to sites of data practices, relying upon documentary analysis to inform empirical investigation (Hogan, 2015;van der Vlist, 2016;Williamson, 2015). Yet, in order to contribute to the development of alternative futures in which 'publics might be said to have greater agency and reflexivity vis-a`-vis data power' (Kennedy and Moss, 2015), it is important that critical 'Big Data' research gets 'under the hood' to grasp how local and situated 'Big Data' practices structure how data work in the world, and thus how particular practices, and their social consequences, might be ameliorated.…”

Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence

“…Algorithms have gained importance in decision-making (Clark et al, 2007), although decision-making is rooted in the assumption that decisions rely on human competencies like knowledge and human experience (Newell and Marabelli, 2015; Shollo and Galliers, 2016). The function of algorithms in decision-making has moved from descriptive to predictive modes of data analytics and to the prescription of best options for actions in operational and strategic domains (Van der Vlist, 2016). In this context, learning algorithms, often referred to as artificial intelligence (AI) or ‘cognitive systems’ (Helbing, 2019), are finding their way into workplace decisions.…”

Social Media and Platform Historiography: Challenges and Opportunities

“…62 Complementary sources such as open source software repositories, research publications, and technical reports may provide further insights into the design rationales of these architecture styles and standards. 63 In conclusion, the developer pages, and especially reference documentation, provide significant research opportunities for historical platform studies from a technical perspective and beyond, as they provide important entry points to examine concerns around data privacy and data justice. Additionally, they highlight the interplay between the multiple sides and layers of platforms, providing insights into their operative scales and scopes: from the level of data types and formats to API specifications and design rules to architecture design to the technical conditions of large-scale platform ecosystems that have been gradually woven into the technical fabric of the web and mobile ecosystem through APIs and SDKs and their modules.…”