Data journalism and misinformation
Oscar Westland and Alfred Hermida
Journalism, news publishers, and journalists are often perceived as having essential roles in society and for democracy. Journalists are assumed to provide verified knowledge daily about public affairs and current events (Carlson 2017). In many countries, there is freedom of speech and freedom of the press, with journalists and news publishers having significant autonomy and professional routines for news production. Journalism maintains a position as one of the most influential knowledge-producing institutions in society, though its role clearly varies substantially around the world. In some countries, journalism is well resourced and able to scrutinise those in power, whereas in others, the authorities exert a substantial degree of control and censorship. News publishers engage in various epistemologies of journalism, involving the production of written news stories, live blogging, and broadcasting, as well as turning to data to identify and report on important patterns and developments. Amid the growing significance of platforms there is an overall process of dislocation, where news is separated from journalism (Ekstrom & Westlund, 2019a).
This chapter focuses on the intersection of data journalism and misinformation by discussing research into epistemic practices for finding and working with data as well as how data is used to make claims about events and public affairs. Data journalism, computer-assisted reporting, and computational journalism are various conceptualisations used to track the quantitative turns in journalism over time (Coddington 2015). Behind these terms is an epistemology of the evidential forte of data-driven journalism, with journalists as ‘apostles of certainty’ (Anderson 2018). Data journalism draws on fields such as information visualisation, computer science, and statistics to convey news through the analysis and representation of quantitative, computer-processed data. Data journalism has continuously expanded around the world and to the global South (Mutsvairo, Bebawi & Borges-Key 2019).
In traditional news journalism, journalists have often relied on established networks of sources that are mainly composed of known institutions and elites (Ettema, James & Glasser 1987). While journalists have often been content as long as they selected and reported the opinions of seemingly reliable sources (and in contrast to each other when needed), their claims to the truth sometimes go no further than assuring ‘he says’ versus ‘she says’ among seemingly reliable sources. This is obviously incredibly problematic as it means journalists offer prominent exposure to presidents and prime ministers who repeatedly articulate false claims, whether misinformation or disinformation. Fortunately, there is not just one universal form of journalism but several genres and epistemologies of (digital) journalism (Ekstrom & Westlund 2019b), taking a more distinct form in the case of, for example, live blogging compared to traditional news journalism (Matheson & Wahl-Jorgensen 2020; Thorsen & Jackson 2018). Ekstrom and Westlund (2019b) write that ‘epistemology is the study of knowledge: what we know, how we know, and how knowledge is justified’ (1), referring to the standards, norms, and methods that journalists use when deciding when information is reliable and truthful and when it is not.
While data journalism has been envisioned to advance solid ways of knowledge in society, it is contingent on factors such as access to datasets, reliable and representative data, and individuals (journalists) with the skills to understand and analyse the data (Cairo 2015). In the best case, data journalists can employ data to reveal and visualise complex phenomena in ways that advance journalism practice and spread important knowledge. In the worst case, journalists and news publishers end up publishing data journalism that skews information and spreads misinformation.
Misinformation is produced and shared by a great number of actors (Napoli 2020; Quandt 2018; Tandoc, Lim & Ling 2018) and is published and shared across digital platforms. This connects with alternative news media that situated themselves as a counter to established news publishers gaining ground (Boberg, Quandt, Schatto-Eckrodt & Frischlich 2020; Figenschou & Ihlebaek 2019; Holt, Ustad Figenschou & Frischlich 2019). There are outright ‘fake news’ producers that imitate some journalistic practices and the style of news content (Robertson & Mourào 2020) and use bots to fuel programmatic advertising revenues (Braun & Eklund 2019). Moreover, to political leaders and the public, ‘fake news’ is also a label used to delegitimise others, including, but not limited to, the institutions of journalism (Egelhofer & Lecheler 2019). ‘Disinformation’ refers to situations in which actors deliberately, driven by political and/or economic interests, produce and distribute information intended to disinform for their own ends. ‘Misinformation’ refers to information that is inaccurate and/or false but where there is no intention to mislead. The category of misinformation extends to the public, authorities, academics, and journalists who unintentionally produce and/or spread misleading, inaccurate, or false information. Journalists obviously are expected to seek out and verify important claims with different reliable sources, but that does not mean they always succeed. Scholars have long since questioned whether journalists actually can achieve fundamental levels of‘accuracy’ (Compton & Benedetti 2010; Shapiro, Brin, Bédard-Brülé & Mychajlowycz 2013).
The next section focuses on two main epistemological dimensions at the intersection of data journalism and misinformation. First, what do (data) journalists know, and how do they know it, in the context of their norms, practices, and routines? Second, how are knowledge claims made in, and in relation to, data journalism materials? This chapter will draw on examples of data journalism and misinformation related to the COVID-19 pandemic.