'Research note: The scale of Facebook’s problem depends upon how ‘fake news’ is classified' by Richard Rogers in (2020) Misinformation Review comments
Ushering in the contemporary ‘fake news’ crisis, Craig Silverman of Buzzfeed News reported that it outperformed mainstream news on Facebook in the three months prior to the 2016 US presidential elections. Here the report’s methods and findings are revisited for 2020. Examining Facebook user engagement of election-related stories, and applying Silverman’s classification of fake news, it was found that the problem has worsened, implying that the measures undertaken to date have not remedied the issue. If, however, one were to classify ‘fake news’ in a stricter fashion, as Facebook as well as certain media organizations do with the notion of ‘false news’, the scale of the problem shrinks. A smaller scale problem could imply a greater role for fact-checkers (rather than deferring to mass-scale content moderation), while a larger one could lead to the further politicization of source adjudication, where labelling particular sources broadly as ‘fake’, ‘problematic’ and/or ‘junk’ results in backlash.
Rogers' research questions were
- To what extent is ‘fake news’ (as defined in the 2016 seminal news article) present in the most engaged-with, election-related content on Facebook in the run-up to the 2020 US presidential elections?
- How does the current ‘fake news’ problem compare to that of the 2016 election period, both with the same as well as a stricter definition of ‘fake news’?
- How does the scale of the problem affect the viability of certain approaches put forward to address it?
- Is there more user engagement with hyperpartisan conservative or progressive sources in political spaces on Facebook?
- How does such engagement imply a politicization of the ‘fake news’ problem?
The ‘fake news’ problem around the US elections as observed in 2016 has worsened on Facebook in 2020. In the early months in 2020 the proportion of user engagement with ‘fake news’ to mainstream news stories is 1:3.5, compared to 1:4 during the same period in 2016. It is both an observation concerning the persistence of the problem and an admonition that the measures undertaken to date have not lessened the phenomenon. If one applies a stricter definition of ‘fake news’ such as only imposter news and conspiracy sites (thereby removing hyperpartisan sites as in Silverman’s definition), mainstream sources outperform ‘fake’ ones by a much greater proportion.
The findings imply that how one defines such information has an impact on the perceived scale of the problem, including the types of approaches to address it. With a smaller-scale problem, fact-checking and labelling become more viable alongside the ‘big data’ custodial approaches employed by social media firms.
Given there are more hyperpartisan conservative sources engaged with than hyperpartisan progressive ones, the research points to how considerations of what constitutes ‘fake news’ may be politicized.
The findings are made on the basis of Facebook user engagement of the top 200 stories returned for queries for candidates and social issues. Based on existing labelling sites, the stories and by extension the sources are classified along a spectrum from more to less problematic and partisan.
The initial ‘fake news’ crisis (Silverman, 2016; 2017) had to do with fly-by-night, imposter, conspiracy as well as so-called ‘hyperpartisan’ news sources outperforming mainstream news on Facebook in the run up to the 2016 US presidential elections. In a sense it was both a critique of Facebook as ‘hyperpartisan political-media machine’ (Herrman, 2016) but also that of the quality of a media landscape witnessing a precipitous rise in the consumption and sharing of ‘alternative right’ news and cultural commentary (Benkler et al., 2017; Holt et al., 2019). The events of the first crisis have been overtaken by a second one where politicians as President Trump in the US and elsewhere employ the same term for certain media organizations in order to undermine their credibility. Against the backdrop of that politicization as well as rhetorical tactic, scholars and platforms alike have demurred using the term ‘fake news’ and instead offered ‘junk news,’ ‘problematic information,’ ‘false news’ and others (Vosoughi et al., 2018). Some definitions (as junk news and problematic information) are roomier, while others are stricter in their source classification schemes.
Subsumed under the original ‘fake news’ definition are imposter news, conspiracy sources and hyperpartisan (or ‘overly ideological web operations’) (Herrman, 2016), and the newer term ‘junk news’ covers the same types of sources but adds the connotation of attractively packaged junk food that when consumed could be considered unhealthy (Howard, 2020; Venturini, 2019). It also includes two web-native source types. ‘Clickbait’ captures how the manner in which it is packaged or formatted lures one into consumption, and ‘computational propaganda’ refers to dubious news circulation by bot and troll-like means, artificially amplifying its symbolic power. Problematic information is even roomier, as it expands its field of vision beyond news to cultural commentary and satire (Jack, 2017). Stricter definitions such as ‘false news’ would encompass imposter and conspiracy but are less apt to include hyperpartisan news and cultural commentary, discussing those sources as ‘misleading’ rather than as ‘fake’ or ‘junk’ (Kist & Zantingh, 2017).
Rather than an either/or proposition, ‘fake news’ could be understood as a Venn diagram or matryoshka dolls with problematic information encompassing junk news, junk news fake news, and fake news false news (Wardle, 2016; 2017). (While beyond the scope, the definition could be broadened even further to include more media than stories and sources, such as video and images.)
Depending on the definition, the scale of the problem changes as does the range of means to address it. With ‘false news’, it grows smaller, and fact-checking again would be a profession to which to turn for background research into the story and the source. Fact-checking has been critiqued in this context because of the enormity of the task and the speed with which the lean workforces must operate. Facebook for one employs the term ‘false news’ and has striven to work with fact-checking bodies, though its overall approach is multi-faceted and relies more on (outsourced) content reviewers (Roberts, 2016; Gillespie, 2018). Other qualitative approaches such as media literacy and bias labelling are also manual undertakings, with adjudicators sifting through stories and sources one by one. When the problem is scaled down, these too become viable.
Roomier definitions make the problem larger and result in findings such as the most well-known ‘fake news’ story of 2016. ‘Pope Francis Shocks World, Endorses Donald Trump for President’ began as satire and was later circulated on a hyperpartisan, fly-by-night site (Ending the Fed). It garnered higher engagement rates on Facebook than more serious articles in the mainstream news. When such stories are counted as ‘fake’, ‘junk’ or ‘problematic’, and the scale increases, industrial-style custodial action may be preferred such as mass contention moderation as well as crowd-sourced and automated flagging, followed by platform escalation procedures and outcomes such as suspending or deplatforming stories, videos and sources.
As more content is taken down as a result of roomy source classification schemes, debates about freedom of choice may become more vociferous rather than less. It recalls the junk food debate, and in this regard, Zygmunt Bauman stressed how we as homo eligens or ‘choosing animals’ are wont to resist such restrictions, be it in opting for ‘hyperprocessed’ food or hyperpartisan news and cultural commentary (2013).
Labelling hyperpartisan news as ‘fake’ or ‘junk’, moreover, may lead to greater political backlash. Indeed, as our findings imply, the ‘fake news’ or ‘junk news’ problem is largely a hyperpartisan conservative source problem, whereas the ‘false news’ one is not. As recently witnessed in the Netherlands, the designation of hyperpartisan conservative sources as ‘junk news’ drew the ire of the leader of a conservative political party, who subsequently labelled mainstream news with the neologism, ‘junk fake news’ (Rogers & Niederer, 2020; Van Den Berg, 2019). Opting for the narrower ‘false news’ classification would imply a depoliticization of the problem.