Tag Archives: computational social science

Algorithmic Intelligence? Reconstructing Citizenship through Digital Methods



Screen Shot 2016-04-12 at 7.56.00 AMIn the next post for ‘The Person in the (Big) Data‘ edition, Chris Birchall @birchallchris talks us through a variety of methods – big, small and mixed – that he used to study citizenship in the UK. Using some of the dominant tools for studying large data sources in one part of the study, Chris realised that the tools used had a significant impact on what can be (and is being) discovered and that this is quite different from the findings reached by deeper, mixed methods analysis. In this post, Chris asks important questions about whether big data research tools are creating some the conditions of citizenship today and what, exactly, deeper, more nuanced analysis can tell us.

People talk about politics online in many different ways and for many different purposes. The way that researchers analyse and understand such conversation can influence the way that we depict public political opinion and citizenship. In two recent projects I investigated the nature of this conversation and the forces that influence it, as well as the networks, spaces and resources that link that talk to political action. In doing so, I encountered a methodological rift in which careful, manual, time consuming approaches produce different types of conclusions from the big data driven approaches that are widespread in the commercial social media analytics industry. Both of these approaches could be framed as an illustration of human behaviour on the internet, but their differences show that the way that we embrace big data or digital methods influences the understanding of digital publics and citizenship that we gain from the translation of mass online data.

My recently submitted PhD study investigated online public political conversation in the UK. Drawing on the work of previous scholars who have focussed on the deliberative online public sphere (such as Coleman and Gotze, 2001; Coleman and Moss, 2012; Mutz, 2006; Wright and Street, 2007; Graham, 2012), the study acknowledged the importance of interpersonal exchange between participants and exposure to diverse and opposing viewpoints in the formation of preferences and informed opinion. My initial motivation was to ask how interface design might influence people as they talk about politics in online spaces, but this required an examination of the more human, less technologically determinate factors that are also, and often more significantly, involved in political expression.

Over the course of the study it became obvious that the methodology used to investigate these concepts influences the insight obtained; something that many researchers have discussed in the context of digital methods within social science (Baym, 2013; Boyd and Crawford, 2012; Clough et al., 2015; Gitelman and Jackson, 2013; Kitchin and Lauriault, 2014; Kitchin, 2014; Manovich, 2011; Van Dijck, 2014). Technologically mediated questions can be answered through technology-centric methods to give technologically focussed answers, while questions involving human nature, motivation and interaction can be answered by qualitative, human-centred methods in order to provide human-centred answers. These approaches represent the divide between the large scale, quantitative analysis of big data methods and small scale qualitative approaches. In order to address this issue, I employed a methodology which was designed to combine these approaches through directed iterations of analysis that was initially large scale and quantitative, but increasingly small scale and qualitative.Read More… Algorithmic Intelligence? Reconstructing Citizenship through Digital Methods

The Person in the (Big) Data


FullSizeRender This edition of EM is jam-packed with methods for doing people-centred digital research and is edited by EM co-founder Heather Ford (@hfordsa)
newly-appointed Fellow in Digital Methods at the University of Leeds and thus super excited to understand her role as an ethnographer who (also) does digital methods.

Today we launch the next edition of Ethnography Matters entitled: ‘Methods for uncovering the Person in the (Big) Data’. The aim of the edition is to document some of the innovative methods that are being used to explore online communities, cultures and politics in ways that connect people to the data created about/by them. By ‘method’, we mean both the things that researchers do (interviews, memo-ing, member checking, participant observation) as well as the principles that underpin what many of us do (serving communities, enabling people-centred research, advocating for change). In this introductory post, I outline the current debate around the risks of data-centric research methods and introduce two principles of people-centric research methods that are common to the methods that we’ll be showcasing in the coming weeks.

As researchers involved in studying life in an environment suffused by data, we are all (to at least some extent) asking and answering questions about how we employ digital methods in our research practice. The increasing reliance on natively digital methods is part of what David Berry calls the “computational turn” in the social sciences, and what industry researchers recognize as moves towards Big Data and the rise of Data Science.

digitalmethods

Digital Methods‘ by Richard Rogers (2013)

First, a word on digital methods. In his groundbreaking work on digital methods, Richard Rogers argued for a move towards natively digital methods. In doing so, Rogers distinguishes between methods that have been digitized (e.g. online surveys) vs. those that are “born digital” (e.g. recommender systems), arguing that the Internet should not only be seen as an object for studying online communities but as a source for studying modern life that is now suffused by data. “Digital methods,” writes Rogers, “strives to follow the evolving methods of the medium” by the researcher becoming a “native” speaker of online vocabulary and practices.

The risks of going natively digital

There are, however, risks associated with going native. As ethnographers, we recognize the important critical role that we play of bridging different communities and maintaining reflexivity about our research practice at all times and this makes ethnographers great partners in data studies. Going native in this context, in other words, is an appropriate metaphor for both the benefits and risks of digital methods because the risk is not in using digital methods but in focusing too much on data traces.

Having surveyed some of debates about data-centric methodology, I’ve categorized the risks according to three core themes: 1. accuracy and completeness, 2. access and control, 3. ethical issues.Read More… The Person in the (Big) Data