The Challenges of Researching Algorithms

In the debate on algorithmic accountability, and platform responsibility more specifically, the contribution of the social researcher is immense. In this set of posts, researchers reflect upon broad themes of control and agency — not only that which is faced by the data subject, but also by the researcher who relies on proprietary platforms to understand how these systems operate and interact with users. This research bears relevance to policy debates, because it provides evidence of ways in which automated systems shape consumer and citizens and look beyond conventional recommendations of transparency or openness

Whatsapp und Snapchat

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Partizipative Zahlen. Vom Wert der Likes

Für diesen Beitrag ist leider kein Abstract verfügbar. ----------URN: urn:nbn:de:hbz:6:3-201403144

Testing and not testing for coronavirus on Twitter : surfacing testing situations across scales with interpretative methods

How was testing—and not testing—for coronavirus articulated as a testing situation on social media in the Spring of 2020? Our study examines everyday situations of Covid-19 testing by analyzing a large corpus of Twitter data collected during the first 2 months of the pandemic. Adopting a sociological definition of testing situations, as moments in which it is no longer possible to go on in the usual way, we show how social media analysis can be used to surface a range of such situations across scales, from the individual to the societal. Practicing a form of large-scale data exploration we call “interpretative querying” within the framework of situational analysis, we delineated two types of coronavirus testing situations: those involving locations of testing and those involving relations. Using lexicon analysis and composite image analysis, we then determined what composes the two types of testing situations on Twitter during the relevant period. Our analysis shows that contrary to the focus on individual responsibility in UK government discourse on Covid-19 testing, English-language Twitter reporting on coronavirus testing at the time thematized collective relations. By a variety of means, including in-memoriam portraits and infographics, this discourse rendered explicit challenges to societal relations and arrangements arising from situations of testing and not testing for Covid-19 and highlighted the multifaceted ways in which situations of corona testing amplified asymmetrical distributions of harms and benefits between different social groupings, and between citizens and state, during the first months of the pandemic

"Girls are like Glass": Situated Knowledges of Syrian Refugee Women on Datafication and Transparency

This chapter focuses on Syrian refugee women as data subjects in the bureaucratic system of the Dutch immigration services (IND). In an increasingly datafied society many aspects of governance are becoming subject to some form of datafication. The same goes for the decision-making process of the immigration services. Recent research on these processes has mainly focussed on data practices by the European Union in order to protect "Fortress Europe" or use by Syrian refugees themselves of social media and telephone. Media and social research on immigration practices has mainly focussed on inequality and the representation of refugees, in society, in policy-making and in the process of integration. This chapter combines a top-down perspective (data system) with a bottom-up perspective (data subjects) on the IND’s data system by integrating an analysis of data and information about Syrian refugee women present in the IND system with the experiences of the women that provided the information. The result is a moving as well as very informative collection of responses, experiences and insights of five Syrian women refugee women who are in, or have been through the IND’s decision-making process and who speak back to the system, producing alternative knowledge and representations to the dominant and mainstream stories of migration and integration in the Netherlands

Interface Methods: Renegotiating relations between digital social research, STS and sociology

This paper introduces a distinctive approach to methods development in digital social research called “interface methods.” We begin by discussing various methodological confluences between digital media, social studies of science and technology (STS) and sociology. Some authors have posited significant overlap between, on the one hand, sociological and STS concepts, and on the other hand, the ontologies of digital media. Others have emphasised the significant differences between prominent methods built into digital media and those of STS and sociology. This paper advocates a third approach, one that a) highlights the dynamism and relative under-determinacy of digital methods, and b) affirms that multiple methodological traditions intersect in digital devices and research. We argue that these two circumstances enable a distinctive approach to methodology in digital social research – thinking methods as ‘interface methods’ - and the paper contextualizes this approach in two different ways. First, we show how the proliferation of online data tools or ‘digital analytics’ opens up distinctive opportunities for critical and creative engagement with methods development at the intersection of sociology, STS and digital research. Second, we discuss a digital research project in which we investigated a specific ‘interface method’, namely co-occurrence analysis. In this digital pilot study we implemented this method in a critical and creative way to analyse and visualise ‘issue dynamics’ in the area of climate change on Twitter. We evaluate this project in the light of our principal objective, which was to test the possibilities for the modification of methods through experimental implementation and interfacing of various methodological traditions. To conclude, we discuss a major obstacle to the development of ‘interface methods’: digital media are marked by particular quantitative dynamics that seem adverse to the methodological commitments of sociology and STS. To address this, we argue in favour of a methodological approach in digital social research that affirms its mal-adjustment to the research methods that are prevalent in the medium