Navigating Big Data dilemmas: Feminist holistic reflexivity in social media research (original) (raw)
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Social Media + Society/, 2018
In this article, we seek to problematize assumptions and trends in “big data” digital methods and research through an intersectional feminist lens. This is articulated through a commitment to understand how a feminist ethics of care and Donna Haraway’s ideas about “situated knowledge” could work methodologically for social media research. Taking up current debates within feminist materialism and digital data, including big, small, thick, and “lively” data, the argument addresses how a set of coherent feminist methods and a corollary epistemology is being rethought in the field today. We consider how the “queering” of Hannah Arendt’s concept of “action” could contribute to a critically optimistic and inclusive reflection on the role of ethical political commitments to the subjects/objects of study imbricated in big data. Finally, we use our recent research to pose a number of practical questions about practices of care in social media research, pointing toward future research directions.
From data points to people: feminist situated ethics in online big data research
International Journal of Social Research Methodology, 2019
Many ethical concerns in online big data research stem from a pervasive assumption that data are disembodied and place-less. While some scholars have begun addressing the ethical dilemmas of big data, few offer approaches or tools that fully grapple with the situatedness of online data and its ethical implications. We draw on feminist new materialist scholars to interrogate the onto-epistem-ological assumptions of online big data research and explicate their ethical implications. We then use Donna Haraway's work as a theoretical foundation for a reimagining of online data as embodied and situated, putting forth a 'feminist situated ethics' as an alternative ethical framework for online big data research. Finally, we discuss how to enact feminist situated ethics at the different stages of the research process to provide guidance for online big data researchers.
The Magic of Completeness and the Politics of Invisibility: A Feminist Response to Big Data
In recent years proponents of Big Data methodology have declared a revolution in social science methodology. They state that now that we have ‘all the data’, we no longer need to worry about questions like representativeness, and argue that every important social interaction can be seen, measured and studied through big data. As a result, they claim that policymaking will be more scientific and more responsive to citizen needs. However, those who were unrepresented or underrepresented in traditional positivist scientific methods are even more invisible in Big Data. This serious oversight casts doubt on the utility and equity of this methodology. This paper puts forth three critiques of Big Data – raising questions of authenticity, representativeness and meaning.
Acta Baltica Historiae et Philosophiae scientiarum, 2020
Emerging digital data sources provide opportunities for explaining social processes, but also challenge knowledge production practices within social sciences. this article contributes to the ‘end of theory’ discussions, which have intensified in the social sciences since the widening practice of big data and computational methods. Adopting a systematic literature review of 120 empirical articles through a combined quantitative and qualitative approach, this article strives to contribute to the ongoing discussions on the epistemological shifts in social media big data (smBD) studies. This study offers an insight into the development of analytical methods and research practices in smBD studies during their rapid growth period in 2012 –2016. The study findings only partially revealed the ‘end of theory’ claim: the problem setting of the studies is rather weakly related to theory, often neither hypothesis nor research questions are formulated on the basis of previous theories or research. However, this relatively weak relatedness to theory has not led to the descriptive type of inference, but rather exploratory, or predictive ways of reasoning. instead of enabling predictions in social science research, smBD raises issues of understanding the causes and effects in predictions for evaluating the social mechanisms of global disruptions. Developing ‘human research machines’ that exploit the cognitive resources of individuals should not be the aim of smBD production. the outcome should be to recognise that the cognitive abilities of researchers, access to data, and developing novel methods are necessary for evaluating the global impact of social behaviour.
Introduction: Politics of Big Data Special Issue, Digital Culture & Society 2:2, 2016
This special issue offers a critical dialogue around the myriad political dimen-sions of Big Data. We begin by recognising that the technological objects of Big Data are unprecedented in the speed, scope and scale of their computation and knowledge production. This critical dialogue is grounded in an equal recogni-tion of continuities around Big Data’s social, cultural, and political economic dimensions. Big Data, then, is political in the same way in which identity, the body, gender, sexuality, race and ethnicity are political, that is, as sites of struggle over meaning, interpretations, and categorisations of lived experience. Big Data is political in the way circuits of production, distribution, and consumption are political; that is, as sites where access, control and agency are unequally distrib-uted through asymmetrical power relations, including relations of data produc-tion. Big Data is political in the way contemporary politics are being reshaped by data analysis in electoral campaign strategy, and through state surveillance as strikingly evidenced by the Snowden revelations on the NSA and GCHQ. Big Data is also political in the contestation of this advanced scientific practice, wherein the generation of data at unprecedented scale promises a precise and objective measure of everyday life. However, the computational dreams of an N = all verisimilitude – that is, of datasets providing a one-to-one correspon-dence to a given phenomenon – are haunted by the normative biases embedded in all data. This is not to suggest that Big Data – more specifically processes of datafication1 – are best or at all understood as socially constructed. Indeed, discursive analysis or unreconstructed social theory cannot fully grasp how data re-articulates the social, cultural, political and economic in a deeply recursive manner. Thus, any political reckoning must equally account for the materiality of data, alongside the logic guiding its processes and the practices that deploy its tools. In short, what are the power relations animating the knowledge generated by data analytics?
Qualitative Research Ethics in the Big Data Era
American Behavioral Scientist
This article examines the developments that have motivated this special issue on Qualitative Research Ethics in the Big Data Era. The article offers a broad overview of many pressing challenges and opportunities that the Big Data era raises particularly for qualitative research. Big Data has introduced to the social sciences new data sources, new research methods, new researchers, and new forms of data storage that have immediate and potential effects on the ethics and practice of qualitative research. Drawing from a literature review and insights gathered at a National Science Foundation-funded workshop in 2016, we present five principles for qualitative researchers and their institutions to consider in navigating these emerging research landscapes. These principles include (a) valuing methodological diversity; (b) encouraging research that accounts for and retains context, specificity, and marginalized and overlooked populations; (c) pushing beyond legal concerns to address often ...
The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
Toward a Posthumanist Ethics of Qualitative Research in a Big Data Era
American Behavioral Scientist, 2019
The Big Data phenomenon, and its uptake in qualitative research, raises ethical issues around data aggregation, data linkages, and data anonymization as well as concerns around changing meanings and possibilities of informed consent and privacy protection. In this article, I address the ethical issues that arise from Big Data through a posthumanist philosophical framework. The humanist ethics that underpins normative ethical concerns-as outlined above-focuses on the unequal power relationship between researchers and research subjects and the potential harm that research can cause to research participants. Ethical practice consists in following guidelines and codes of ethical conduct designed, not so much to avoid these power differentials, but to protect research participants from potential exploitation and infringements of their human rights. Unethical research is understood as research that breaches these principles and/or harms its research subjects. A posthumanist ethics treats knowledge-making itself as a matter of ethical concern. It shifts the focus away from the power of researchers over research participants toward the "world-making" powers of practices of inquiry: their ability to constitute (and not simply discover) the very nature of their objects/subjects of study. Its focus of ethical concern-what it regards as unethical-is research that claims to represent the world "as it really is." On this approach, ethical practice consists in accounting for the ways in which research ontologically constitutes its objects and subjects of study. The critical intervention made possible by bringing a posthumanist perspective to bear on the ethics of qualitative research in a Big Data era is to foreground Big Data's treatment of data as self-evident, and its positivist claim to represent the world innocently, accurately, and objectively, as matters of ethical concern.
Ghosts of white methods? The challenges of Big Data research in exploring racism in digital context
Big Data & Society
The paper explores the potential and limitations of big data for researching racism on social media. Informed by critical data studies and critical race studies, the paper discusses challenges of doing big data research and the problems of the so called ‘white method’. The paper introduces the following three types of approach, each with a different epistemological basis for researching racism in digital context: 1) using big data analytics to point out the dominant power relations and the dynamics of racist discourse, 2) complementing big data with qualitative research and 3) revealing new logics of racism in datafied context. The paper contributes to critical data and critical race studies by enhancing the understanding of the possibilities and limitations of big data research. This study also highlights the importance of contextualisation and mixed methods for achieving a more nuanced comprehension of racism and discrimination on social media and in large datasets.
Citizens’ media meets big data: the emergence of data activism
MEDIACIONES
Los big data representan nuevos retos y nuevas oportunidades para la ciudadanía. Las prácticas del “activismo de datos” surgen de la intersección de las dimensiones social y tecnológica de la acción humana, por la cual la ciudadanía adopta una postura crítica hacia los big data, que se apropia y manipula para hacer campaña y promover el cambio social. Este artículo teórico explora el surgimiento del activismo de datos como una realidad empírica y una herramienta heurística para estudiar cómo la gente se relaciona políticamente con los big data. Ponemos en contexto este concepto a través de una revisión de literatura académica y ofrecemos una definición del activismo de datos, así como una agenda tentativa para su estudio. Argumentamos que el activismo de datos representa una nueva forma de medio ciudadano que coloca en su mismo centro una aproximación crítica hacia los big data.