Digital Capitalism

Besides conducting qualitative social research with social media users in order to learn about their experiences, interpretations, and perspectives, critical digital methods should not completely discard tools for digital data collection and analytics, but take their use into a new direction. Critical digital methods should certainly engage in collecting and analysing samples of data from social media platforms with the help of tools and services, such as DiscoverText, Tweet Archivist, Netvizz, NodeXL, Gephi, NCapture/NVivo, Sodato, Import.io, InfoExtractor, Google Web Scraper, TAGS, SocioViz, and so on⁷.

Critical digital methods, however, do not simply apply large-scale quantitative analysis to these data but use smaller samples (“small data” as opposed to “big data”) that are analysed with the help of qualitative methods (critical visual analysis, critical discourse analysis, qualitative text/content analysis, etc.) and interpreted with the help of critical theory. The focus can, for example, be on key topics or the most followed, liked, or retweeted users. In some cases, analysing single users’ postings may be most appropriate; in other cases, focusing on a specific time period, discourse topic, particular group, specific characteristic, and so on may be most suited.

One important aspect of critical social media research is the study of not just ideologies of the Internet but also ideologies on the Internet. Critical discourse analysis and ideology critique as research method have only been applied in a limited manner to social media data. Majid KhosraviNik (2013) argues in this context that “critical discourse analysis appears to have shied away from new media research in the bulk of its research” (p. 292). Critical social media discourse analysis is a critical digital method for the study of how ideologies are expressed on social media in light of society’s power structures and contradictions that form the texts’ contexts.

Discourses tend to be intertextual and interdiscursive (Reisigl and Wodak 2001, p. 39). They interlink various texts, discourses, and contexts. Social media data are therefore not independent from other media but tend to be multimodal and connected with texts in traditional media. An example is that many political tweets tend to link to articles in the online versions of mainstream newspapers. Studying social media therefore does not substitute the study of other media but often requires studying various media’s interconnection. Discourses are texts that stand in particular societal, politicaleconomic, historical, and cultural contexts. Understanding them requires taking a holistic point of view, that is, to situate them in history and society. This means that interpretation, explanation, evaluation, and critique of discourses require theories of society (Fairclough 2015). This, for example, means that when a case study of fascist ideology online is conducted, the critical theory and history of fascism will guide the analysis, and a new contribution to theory construction will emerge from the analysis.

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7 See http://socialmediadata.wikidot.com and https://wiki.digitalmethods.net/Dmi/ToolDatabase for overviews.

7.5 Critical-Realist Social Media Research Ethics

Critical digital methods challenge digital positivism. In this context, also ethical questions arise. All critical theories are inherently ethico-political. They reject the assumption that academic knowledge can be value neutral, and argue that all social construction of knowledge reflects power and norms and is therefore political. One of the critical theory’s tasks is to lay open and criticise the ideologies inherent in specific knowledge. Critical theory’s own knowledge is aimed at contributing to a better understanding of society’s power contradictions and how to overcome them in order to create a better world.

But there is not just the role of ethics in research but also the ethics of conducting research. Research ethics concern issues, such as privacy, anonymity, informed consent, and the sensitivity of data. Given that social media is part of society’s tendency to liquefy and blur the boundaries between the private and the public, labour/leisure, production/consumption (Fuchs 2015a, chapter 8), research ethics in social media research is particularly complex. The complexity and confusion resulting from Internet research are, for example, evident in the British Sociological Association’s Statement of Ethical Practice. It is very unspecific about Internet research ethics, arguing that one should “take special care when carrying out research via the Internet” and be “familiar with ongoing debates on the ethics of Internet research” (British Sociological Association, 2002, §41). Zimmer and Proferes (2014) analysed 382 academic works that studied Twitter data. One hundred and sixty-eight (44.0%) analysed more than 100,000 tweets, and 216 more than 10,000 (56.5%). Only 4% (16) discussed ethical aspects.

Social media research ethics face a contradiction between big data positivism and research ethics fundamentalism. Big data positivists tend to say, “Most social media data is public data. It is like data in a newspaper. I can therefore gather big data without limits. Those talking about privacy want to limit the progress of social science”. This position disregards any engagement with ethics and has a bias towards quantification. The ethical framework Social Media Research: A Guide to Ethics (Townsend and Wallace 2016) that emerged from an ESRC-funded project tries to avoid both extremes and to take a critical-realist position: it recommends that social scientists neither ignore nor fetishise research ethics when studying digital media.

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Research ethics fundamentalists in contrast tend to say,

You have to get informed consent for every piece of social media data you gather because we cannot assume automatic consent, users tend not to read platform’s privacy policies, they may assume some of their data is private and they may not agree to their data being used in research. Even if you anonymize the users you quote, many can still be identified in the networked online environment.

There are, however, limits of informed consent in critical research. It can act a censorship of critical research. Imagine you study online fascism or online harassment of women. In most of such cases, it is not feasible but rather dangerous for the researcher to ask for informed consent. “Dear Mr. Misogynist/Nazi, I am a social researcher gathering data from Twitter. Can you please give me the informed consent that I quote your violent threat against X?”. Asking for informed consent in critical research can result in the circumstance that the researcher herself/himself becomes subject to violence or threats.

Some observers recommend to only use aggregated data and not to quote from social media data. Such an approach has, however, a bias towards quantitative methods, and critical social media discourse analysis thereby becomes impossible to conduct. In social media analysis, privacy’s context matters (Nissenbaum 2010). We cannot assume that social media data analysis can never cause harm and that therefore anything goes in data analytics. At the same time, privacy fundamentalism risks to paralyse critical social media analysis. A realistic approach is needed. We need critical-realist social media research guidelines that go beyond research ethics fundamentalism and beyond big data positivism.

The British Psychological Society (2009) argues that online observation should only take place when and where users “reasonably expect to be observed by strangers” (p. 13). This criterion is feasible in many contexts. It implies, for example, that the use of a popular Twitter hashtag during specific mass events (e.g. the use of the hashtag #BBCDebate during the EU referendum campaign’s first televised TV debate) is aimed at public outreach, so that the user cannot reasonably expect to remain unobserved. It would therefore be acceptable to quote these tweets without asking for informed consent. Whether to obtain informed consent or not in critical social media research depends on the specific contexts of data.

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For Roy Bhaskar, critical moral realism is part of the philosophy of critical realism. It assumes that “moral propositions can be known; and, in particular, social-scientifically vindicated” (Bhaskar 2008, 242). In critical social media research, the user expresses moral values online, and the researcher has a critical attitude towards power structures. The morality and ideologies underlying the online expression can be laid open:

Now the subject-matter of social science is composed not just by social objects but by beliefs about social objects, and if such beliefs are false (a judgment which is within the remit of social science), and one can explain the falsity, then, subject to a ceteris paribus clause, in virtue of the openness of the social world and the multiplicity of determinations therein, one can move without further ado to a negative evaluation of the explanans and a positive evaluation of any action rationally designed to absent it. (Bhaskar 2008, 244)

Critical moral realism in social media research means that the research tries to create knowledge about social media that helps understanding what is absent in the world and needs to be created (absenting absences), in order to foster participatory democracy, freedom, justice, fairness, and equality. This approach neither overdoes nor underdoes research ethics.

7.6 Conclusion

Digital media has become an important topic of study, as the emergence of the field of Internet research shows. It is, however, not just a research topic but has influenced the social sciences in general. Big data analytics and computational social science are the newest developments in this respect. Their emergence is a reflection of the general tendency that critique and theory have in the past decades become less of a focus in academia, which may be one of the consequences of the pressures exerted by neoliberalism, managerialism, and political pragmatism on academia. Effects of instrumental reason in the social sciences include high-level specialisation that is blind for society’s big picture, a disregard for social philosophy, and management and business studies’ influence on the social sciences. The search for a computational social science paradigm is another effect of the diminishing relevance of critique. The danger is that computer science colonises the social sciences and tries to turn them into a subdomain of computer science. There should certainly be co-operations between computer scientists and social scientists in solving societal problems, but co-operation is different from a computational paradigm. Turning social scientists into programmers as part of social science methods education would almost inevitably leave no time for engagement with theory and social philosophy, except if one radically increased the duration of study programmes. Programming is not something you learn overnight, but is a timeconsuming field of education. Computer science’s quantitative logic threatens to undermine critical social science. An alternative paradigm that does not reject, but critically scrutinise the digital, is needed. Critical theory is an alternative interdisciplinary approach that is of importance for both the social sciences and computer science.

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Money is an important means for distributing power, positions, reputation, and influence in academia. Research funding is the academic system’s key monetary resource. In social media research, we can find a distributional inequality of funding: There tend to be special programmes focused on funding big data analytics. This approach is a form of digital positivism and administrative research.

I have, in this chapter, argued for a paradigm shift from administrative, positivist big data analytics towards critical digital/social media research that combines critical social media theory, critical digital methods, and critical-realist social media research ethics. Advancing such a paradigm is a material question whose solution requires not just changes of attitudes but also institutional transformations and a change of funding practices.

Acknowledgement

This chapter has benefited from the discussion at the European Journal of Communication (EJC) symposium on social media (University of Minho, Braga, Portugal, 26–27 May 2016). I thank the editors for the invitation and organisation and all participants for interesting discussions.

References

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Chapter Eight

Social Media, Big Data, and Critical Marketing

8.1 Introduction

8.2 Key Theoretical Approaches

8.3 Current Key Areas of Research

8.4 Directions for Future Research

8.5 Conclusion

References

8.1 Introduction

Social media and big data have become ubiquitous keywords in everyday life. The term social media is commonly used for social networking sites (e.g. Facebook, Weibo), blogs (e.g. WordPress, Tumblr), micro-blogs (e.g. Twitter), user-generated content sharing sites (e.g. YouTube, Flickr, Instagram), or wikis (e.g. Wikipedia) (Fuchs 2017, chapter 2). Big data refers to the collection and analysis of data in such vast quantities that humans are incapable of processing them – only algorithms can (Fuchs 2017, chapter 2). There are diverse sources of big data, one example being credit and debit card transactions. So the term big data is not limited to social media. At the same time, given that Facebook has about 1.8 million monthly active users¹ and Google processes more than 100 billion searches per year on average,² these two US Internet companies are probably the largest data processors in the world. This tells of an inherent link between big data and social media. While social media characterise the techno-social systems enabling human interaction on the Internet, big data are the digital results of human activities. Google, Facebook, and other online platforms tend to store all data and meta-data for long periods of time and therefore require huge server farms consisting of numerous supercomputers.

Google and Facebook are two of the world’s largest companies. In the 2016 Forbes ranking of the world’s 2000 largest transnational corporations, Google (the holding company Alphabet Inc. is now the parent company of Google) occupied rank 27 with its annual profits of US\$ 17 billion. Facebook was on rank 188 with annual profits of US\$

1 Data source: Facebook, SEC Form 10-Q, November 2016

2 Data source: http://www.internetlivestats.com/google-search-statistics/, accessed on November 10, 2016.

DOI: 10.4324/9781003222149-8

3.7 billion. One should not be mistaken: Google and Facebook are not communications companies. They do not sell the ability to communicate. Rather, they are the world’s largest advertising agencies. Their profits almost exclusively derive from targeted advertising. Understanding social media and big data therefore requires that we contextualise these phenomena through the critical study of marketing and advertising.

Critical marketing studies are based on the insight that “marketing has devoted too much attention to refining itself as an instrumental science, with the corollary emphasis on the production of knowledge for the ‘marketing organisation’, not for wider stakeholders” (Tadajewski 2010, 776). Further, it is a “systematic critique of marketing theory and practice” that uses “some form of critical social theory […] whether this is drawn from the neo-Marxist critical theory tradition, some variant of humanism, feminism”, or other approaches (Tadajewski 2010, 774). As a consequence, critical marketing does not mean conducting marketing critically or studying how to make marketing critical. Rather, critique and marketing are polar, dialectical opposites, just like socialism and capitalism. Critical marketing is a critique of marketing that aims at creating knowledge that helps us overcome both capitalism and marketing. Critical marketing studies as a discipline understands itself as being part of an emancipatory social science (Tadajewski and Brownlie, 2008). Taking a critical marketing perspective on social media means to apply critical social theory for understanding social media’s power structures.

The task of this chapter is to critically understand social media and big data’s political economy. It outlines key classical texts (section 8.2), contemporary texts (section 8.3), and future research directions (section 8.4) that can help us achieve this goal.

8.2 Key Theoretical Approaches

There are many critical approaches that matter for critically understanding the Internet and social media. In a text like this chapter, one is necessarily limited to the number of key texts and thinkers one can introduce. I will here focus on four classical thinkers and one text by each of them: Dallas Smythe, Karl Marx, Raymond Williams, and Sut Jhally.

8.2.1 Dallas Smythe: Communications: Blindspot of Western Marxism

Dallas Smythe’s (1977) article Communications: Blindspot of Western Marxism has become a key text in the political economy of communication and when it comes to understanding advertising’s political economy. Smythe’s starting point is a critique of many Marxists’ understanding of communications as transmitters of ideology and of advertising as belonging to an unproductive sphere of capital circulation. “The mass media of communications and related institutions concerned with advertising, market research, public relations and product and package design represent a blindspot in Marxist theory in the European and Atlantic basin cultures” (Smythe 1977, 1). Smythe criticised that a lot of critical and administration scholars analyse commercial media in terms of messages, information, images, meaning, entertainment, orientation, education, manipulation, and ideology. He argues for a perspective that gives a stronger role to the category of labour in the critical study of communication and culture.

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Smythe bases his analysis on Karl Marx’s (1867) insight that the commodity is capitalism’s elementary form and that abstract labour produces the commodity’s value. Smythe asks in the Blindspot essay: What is the advertising-based commercial media’s commodity? Who produces the commercial media’s commodity? Given that advertisingbased media tend to provide their content gratis as a gift, the information cannot be the commodity.

Smythe gave the following answer:

I submit that the materialist answer to the question – What is the commodity form of mass-produced, advertiser-supported communications under monopoly capitalism? – is audiences and readerships (hereafter referred to for simplicity as audiences). […] Of the off-the-job work time, the largest single block is time of the audiences which is sold to advertisers. (Smythe 1977, 3)

Audiences would work to create the demand for monopoly capital’s commodities (Smythe 1977, 6).

Audiences produce attention that is sold as audience commodity to advertisers. Therefore, according to Smythe, audiences conduct unpaid audience labour that produces the audience commodity and is exploited by advertisers. Smythe stressed that in capitalism, also unpaid labour is exploited and produces value. This focus was in line with developments in autonomous Marxism and Marxist feminism in the 1970s: Autonomous Marxists such as Antonio Negri (1988) stressed that there is a collective social worker who creates value inside and outside the factory and the office. They argued that society in capitalism is a social factory. Marxist feminists stressed that housework (re)produces labour-power as a commodity and is therefore exploited by capital (see, e.g. Dalla Costa and James 1973). Smythe’s work, autonomous Marxism, and Marxist feminism have in common that they stress the importance of the exploitation of unpaid labour for capitalism’s existence.

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