This paper reviews the contemporary discussion on the epistemological and ontological effects of Big Data within social science, observing an increased focus on relationality and complexity, and a tendency to naturalize social phenomena. The epistemic limits of this emerging computational paradigm are outlined through a comparison with the discussions in the early days of digitalization, when digital technology was primarily seen through the lens of dematerialization, and as part of the larger processes of “postmodernity”. Since then, the online landscape has (...) become increasingly centralized, and the “liquidity” of dematerialized technology has come to empower online platforms in shaping the conditions for human behavior. This contrast between the contemporary epistemological currents and the previous philosophical discussions brings to the fore contradictions within the study of digital social life: While qualitative change has become increasingly dominant, the focus has gone towards quantitative methods; while the platforms have become empowered to shape social behavior, the focus has gone from social context to naturalizing social patterns; while meaning is increasingly contested and fragmented, the role of hermeneutics has diminished; while platforms have become power hubs pursuing their interests through sophisticated data manipulation, the data they provide is increasingly trusted to hold the keys to understanding social life. These contradictions, we argue, are partially the result of a lack of philosophical discussion on the nature of social reality in the digital era; only from a firm metatheoretical perspective can we avoid forgetting the reality of the system under study as we are affected by the powerful social life of Big Data. (shrink)

How are data science systems made to work? It may seem that whether a system works is a function of its technical design, but it is also accomplished through ongoing forms of discretionary work by many actors. Based on six months of ethnographic fieldwork with a corporate data science team, we describe how actors involved in a corporate project negotiated what work the system should do, how it should work, and how to assess whether it works. These negotiations laid the (...) foundation for how, why, and to what extent the system ultimately worked. We describe three main findings. First, how already-existing technologies are essential reference points to determine how and whether systems work. Second, how the situated resolution of development challenges continually reshapes the understanding of how and whether systems work. Third, how business goals, and especially their negotiated balance with data science imperatives, affect a system’s working. We conclude with takeaways for critical data studies, orienting researchers to focus on the organizational and cultural aspects of data science, the third-party platforms underlying data science systems, and ways to engage with practitioners’ imagination of how systems can and should work. (shrink)

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only (...) from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics. (shrink)

The central thesis of this paper is that human unconscious processes influence the behavior and design of artificial intelligence (AI). This thesis is discussed through the case study of a chatbot called Replika, which intends to provide psychological assistance and friendship but has been accused of inciting murder and suicide. Replika originated from a trauma and a work of mourning lived by its creator. The traces of these unconscious dynamics can be detected in the design of the app and the (...) narratives about it. Therefore, a process of de-psychologization and de-humanization of the unconscious takes place through AI. This psychosocial approach helps criticize and overcome the so-called “standard model of intelligence” shared by most AI researchers. It facilitates a new interpretation of some classic problems in AI, such as control and responsibility. (shrink)

“Digital footprints” is an attractive, useful, and increasingly popular metaphor for thinking about Big Data. In this essay, I elaborate on this metaphor to highlight three relatively basic fallacies in the way we tend to think about Big Data: first, that they contain information on complete populations, or “N = all”; second, that they contain recordings of naturalistic behavior; and third, that they can be understood devoid of context.

Public discourse about the COVID-19 that appears on Twitter and other social media platforms provides useful insights into public concerns and responses to the pandemic. However, acknowledging that public discourse around COVID-19 is multi-faceted and evolves over time poses both analytical and ontological challenges. Studies that use text-mining approaches to analyse responses to major events commonly treat public discourse on social media as an undifferentiated whole, without systematically examining the extent to which that discourse consists of distinct sub-discourses or which (...) phases characterize its development. They also confound structured behavioural data with unstructured user-generated data in their sampling methods. The present study aims to demonstrate how one might go about addressing both of these sets of challenges by combining corpus linguistic methods with a data-driven text-mining approach to gain a better understanding of how the public discourse around COVID-19 developed over time and what topics combine to form this discourse in the Australian Twittersphere over a period of nearly four months. By combining text mining and corpus linguistics, this study exemplifies how both approaches can complement each other productively. (shrink)