European Journal of Communication

Digital media enable processes of datafication: users' online activities leave digital traces that are transformed into data points in databases, kept by service providers and other private and public organisations, and repurposed for commercial exploitation, business innovation, surveillance -- and research. Increasingly, this also extends to sensors and recognition technologies that turn homes and cities, as well as our own bodies, into data points to be collected and analysed So-called ‘traditional’ media industries, too, including public service broadcasting, have been datafied, tracking and profiling audiences, algorithmically processing data for greater personalisation as a way to compete with new players and streaming services. Datafication both raises new research questions and brings about new avenues, and an array of tools, for empirical research. This special issue is dedicated to exploring these, linking them to broader historical trajectories of social science methodologies as well as to central concerns and perspectives in media and communication research. As such, this special issue grapples with approaches to empirical research that interlink questions of methods and tools with epistemology and practice. It discusses the datafication of methods, as well as methods for studying datafication. With this we hope to enable reflection of what research questions media and communication scholars should ask of datafication, and how new and existing methods enable us to answer them.

Extreme, anti-establishment actors are being characterized increasingly as ‘dangerous individuals’ by the social media platforms that once aided in making them into ‘Internet celebrities’. These individuals (and sometimes groups) are being ‘deplatformed’ by the leading social media companies such as Facebook, Instagram, Twitter and YouTube for such offences as ‘organised hate’. Deplatforming has prompted debate about ‘liberal big tech’ silencing free speech and taking on the role of editors, but also about the questions of whether it is effective and for whom. The research reported here follows certain of these Internet celebrities to Telegram as well as to a larger alternative social media ecology. It enquires empirically into some of the arguments made concerning whether deplatforming ‘works’ and how the deplatformed use Telegram. It discusses the effects of deplatforming for extreme Internet celebrities, alternative and mainstream social media platforms and the Internet at large. It also touches upon how social media companies’ deplatforming is affecting critical social media research, both into the substance of extreme speech as well as its audiences on mainstream as well as alternative platforms.

Computational methods offer a new perspective on the evolving agendas of right-wing movements and parties online. This article showcases computational approaches to text analysis (specifically so-called topic models) to diachronically investigate nativist right-wing issues in social media by comparing comments posted on the Facebook page of the Pegida movement to those of the Alternative for Germany. After describing topic modelling as an increasingly popular method and drawing on the literature on right-wing nativism online, we investigate a set of shared issues relevant to the mobilization of commentators, including opposition to Islam, migration, the government and the media. We furthermore show contrastively how issue prevalence differs between the two groups, and how issue shares change over time, in some instances converging on a shared nativist core. We close with a series of suggestions on the utility of computation content analysis for the study of rapidly evolving political agendas.

The article proposes archival thinking as an analytical framework for studying Facebook. Following recent debates on data colonialism, it argues that Facebook dialectically assumes a role of a new archon of public records, while being unarchivable by design. It then puts forward counter-archiving – a practice developed to resist the epistemic hegemony of colonial archives – as a method that allows the critical study of the social media platform, after it had shut down researcher’s access to public data through its application programming interface. After defining and justifying counter-archiving as a method for studying datafied platforms, two counter-archives are presented as proof of concept. The article concludes by discussing the shifting boundaries between the archivist, the activist and the scholar, as the imperative of research methods after datafication.

Anti-corruption efforts from the grassroots that make use of digital media to hinder corrupt behaviors are flourishing worldwide. In many cases, these efforts see activists interact with big data along with other types of data. They do this in the framework of broader communicative infrastructure in which activists create, employ, and spread big data to support their struggles. As well, they do so differently, according to a diverse range of activists’ local situations across the world. The article uses examples of anti-corruption efforts in Brazil, India, and Spain to illustrate how the grounded theory method might help researchers to produce knowledge that escapes a universalistic and global vision of datafication detached from activists’ lived and local experiences. The article first briefly outlines what grounded theory is, the main steps in a grounded theory study, and its applications in media and communication studies. It then moves to a broader discussion of two relevant elements of grounded theory – sensitizing concepts and theoretical sampling – in taking into consideration data-enabled activism as an emergent phenomenon that might take many shapes. Then, it considers the emphasis on the situation in which data-enabled activism spreads out through a brief discussion of one relevant development of grounded theory, which is situational analysis, to solve the tension between the global and the local in data-enabled activism.

This article explores datawalking as a novel method in media and communication research for studying datafication. Drawing from existing literature, datawalking is characterized as an embodied, situated and generative practice. These affordances of walking help to tackle existing research challenges and connect lived experiences to data infrastructural concerns. More specifically, contemporary research on the deep mediatized city faces challenges that pertain to the invisibility, loss of context and access to data and its infrastructures. It is argued that datawalks, as an empirical method in media and communication research, offers a much-needed anchoring of data as material and situated, and constitutive of everyday life.

In the article, we argue that the advent of data mining techniques and big data in media and communication studies present problems that involve fundamental methodological questions, requiring us to revisit existing ways in which the link between theory, operationalization and data are explained and justified. We note that the discourse of instrumental optimization that surrounds big data clouds epistemic debates about their appropriate integration in scholarly explanations, and argue that a discussion of these problems can usefully depart from a distinction between the two main types of data mining models (supervised and unsupervised). We argue that both types pose specific challenges and give examples of ways they have been productively overcome. In particular, we argue that while big data approaches have introduced novel opportunities for research, they have fundamentally been incorporated into media and communication studies in ways that comply with existing, prototypical explanatory schemes. Our examples link specific empirical studies to general strategies of scientific explanation, focusing on neo-positivist, critical realist and interpretivist explanations.