Phillip Brooker

Doing social media analytics

In the few years since the advent of ‘Big Data’ research, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of ‘grand’ social science methodological concepts for new social media research (i.e. they focus on general issues such as sampling, data validity, ethics, etc.). By contrast, we advance an abductively oriented methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool – Chorus – to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions.

Could You Define That in Bot Terms?: Requesting, Creating and Using Bots on Reddit

Bots are estimated to account for well over half of all web traffic, yet they remain an understudied topic in HCI. In this paper we present the findings of an analysis of 2284 submissions across three discussion groups dedicated to the request, creation and discussion of bots on Reddit. We set out to examine the qualities and functionalities of bots and the practical and social challenges surrounding their creation and use. Our findings highlight the prevalence of misunderstandings around the capabilities of bots, misalignments in discourse between novices who request and more expert members who create them, and the prevalence of requests that are deemed to be inappropriate for the Reddit community. In discussing our findings, we suggest future directions for the design and development of tools that support more carefully guided and reflective approaches to bot development for novices, and tools to support exploring the consequences of contextually-inappropriate bot ideas.

Counter-Discourse Activism on Social Media: The Case of Challenging Poverty Porn Television

In this paper we investigate how online counter-discourse is designed, deployed and orchestrated by activists to challenge dominant narratives around socio-political issues. We focus on activism related to the UK broadcast media’s negative portrayal of welfare benefit claimants; portrayals characterised as “poverty porn” by critics. Using critical discourse analysis, we explore two activist campaigns countering the TV programme Benefits Street. Through content analysis of social media, associated traditional media texts, and interviews with activists, our analysis highlights the way activists leverage the specific technological affordances of different social media and other online platforms in order to manage and configure counter-discourse activities. We reveal how activists use different platforms to carefully control and contest discursive spaces, and the ways in which they utilise both online and offline activities in combination with new and broadcast media to build an audience for their work. We discuss the challenges associated with measuring the success of counter-discourse, and how activists rely on combinations of social media analytics and anecdotal feedback in order to ascertain that their campaigns are successful. We also discuss the often hidden power-relationships in such campaigns, especially where there is ambiguity regarding the grassroots legitimacy of activism, and where effort is placed into controlling and owning the propagation of counter-discourse. We conclude by highlighting a number of areas for further work around the blurred distinctions between corporate advocacy, digilantism and grassroots activism.

The value of social media data: Integrating crowd capabilities in evidence-based policy

Abstract Social media have been widely embraced by governments for information dissemination and engagement but less is known about their value as information sources. Crowdsourced content from social media can improve inclusivity in policy development but it is not always clear how it can form part of policy evidence. The paper builds on the conceptual framework of crowd capabilities to examine the value of social media data in evidence-based policy. Acquisition and assimilation – the two elements of crowd capabilities – drive our exploratory case analysis in the context of agricultural policies in the UK. The study combined qualitative data from interviews and workshops with an analysis of networks of farmers on Twitter. Policy makers were broadly positive about the immediacy, cost-effectiveness and diversity of useful input that can be sourced from online sources. Limitations were identified in terms of representation and inclusion of participants in large datasets that are sourced from open platforms. We compare social media data to traditional sources of evidence and further reflect on the new capabilities that can support the needs of policy makers in this endeavor.

Doing stigma: online commenting around weight-related news media

Weight stigma results from the mediatisation of ‘obesity’: conceptually, a medicalised problem resulting from personal bodily irresponsibility. We undertake a frame analysis of 1452 comments on a thematically related online news article published via The Guardian, about the status of ‘obesity’ as a disability in European Union (EU) employment law. We identify three themes: (1) weight as a lifestyle choice or disability, (2) weight as an irresponsible choice and (3) weight as a simple or complex issue. We contend that the design of the commenting platform prevents counter-narratives from challenging the dominant (‘obesity’) framing for three reasons: (1) content is driven by comments appearing earlier in the corpus, (2) the commenting system primarily supports argument between polarised rhetorical positions and (3) the platform design discourages users from developing alternative terminologies for producing counter-narratives. In this way, we explore how weight stigma is propagated through online media, and how users’ comments intersect with the affordances of the platform itself.

Researching with Twitter timeline data: A demonstration via “everyday” socio-political talk around welfare provision:

Increasingly, social media platforms are understood by researchers to be valuable sites of politically-relevant discussions. However, analyses of social media data are typically undertaken by focusing on ‘snapshots’ of issues using query-keyword search strategies. This paper develops an alternative, less issue-based, mode of analysing Twitter data. It provides a framework for working qualitatively with longitudinally-oriented Twitter data (user-timelines), and uses an empirical case to consider the value and the challenges of doing so. Exploring how Twitter users place “everyday” talk around the socio-political issue of UK welfare provision, we draw on digital ethnography and narrative analysis techniques to analyse 25 user-timelines and identify three distinctions in users’ practices: users’ engagements with welfare as TV entertainment or as a socio-political concern; the degree of sustained engagement with said issues, and; the degree to which users’ tweeting practices around welfare were congruent with or in contrast to their other tweets. With this analytic orientation, we demonstrate how a longitudinal analysis of user-timelines provides rich resources that facilitate a more nuanced understanding of user engagement in everyday socio-political discussions online.

What would Wittgenstein say about social media

Much of the excitement in social media analytics revolves around, a) capturing large-scale collections of naturally-occurring talk, b) repurposing them as data, and, c) finding ways to speak sociologically about them. Researchers have raised concerns over the use of social media data in research (for example, boyd and Crawford, 2012; Housley et al, 2014; Tinati et al, 2014), exploring the ontological and epistemological grounding of the emerging field. We contribute to this debate by drawing on Wittgensteinian philosophy to elucidate hitherto neglected aspects; namely that it is not just social scientists who are in the business of analysing social media, but users themselves. We explore how mainstream social media analytics research (1) overinflates the importance of sociological theories, concepts and methodologies (which do not typically feature in the accounts of social media users), (2) downplays the extent to which social media platforms already exhibit order prior to any sociological accounting of them, and, (3) thereby produces findings which explain social scientific perspectives rather than the phenomena themselves. We reformulate the ontological and epistemological basis of social media analytics research from a Wittgensteinian perspective concerned with what it makes sense to say about social media, as members of society and as researchers studying those members. Such a project aims to explore social media users’ language as a practice embedded within the context of social life and online communication. This reflects the everyday use of language as an evolving toolkit for undertaking social interaction, pointing towards an alternative conception of social media analytics.