Testing and unpacking the effects of digital fake news: on presidential candidate evaluations and voter support
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Testing and unpacking the effects of digital fake news: on presidential candidate evaluations and voter support Rodolfo Leyva1 · Charlie Beckett2 Received: 22 December 2019 / Accepted: 13 April 2020 © The Author(s) 2020
Abstract There is growing worldwide concern that the rampant spread of digital fake news (DFN) via new media technologies is detrimentally impacting Democratic elections. However, the actual influence of this recent Internet phenomenon on electoral decisions has not been directly examined. Accordingly, this study tested the effects of attention to DFN on readers’ Presidential candidate preferences via an experimental web-survey administered to a cross-sectional American sample (N = 552). Results showed no main effect of exposure to DFN on participants’ candidate evaluations or vote choice. However, the perceived believability of DFN about the Democratic candidate negatively mediated evaluations of that candidate—especially amongst far-right ideologues. These results suggest that DFN may at worst reinforce the partisan dispositions of mostly politically conservative Internet users, but does not cause or induce conversions in these dispositions. Overall, this study contributes novel experimental evidence, indicating that the potential electoral impact of DFN, although concerning, is strongly conditional on a reciprocal interaction between message receptibility and a pre-existing right-wing ideological orientation. The said impact is, therefore, likely narrow in scope. Keywords Fake news · Priming · Framing effects · Voting · Candidate preferences
1 Introduction Following the 2016 Presidential election victory of Donald Trump, several journalists and politicians argued that the widespread circulation of digital fake news (DFN) via new media technologies played a decisive role in influencing votes and turnout (Mustafaraj and Metaxas 2017; Tandoc et al. 2017). This notion has been given further credence by accusations from American intelligence officials that the Russian government honed and sponsored a sophisticated bombardment of DFN through Facebook and Twitter to sway the election in Trump’s favor. These factors have thus stoked public concern over the possibility that DFN can now be used as an effective weapon to undermine democratic elections across the globe (Vargo et al. 2018).1 * Rodolfo Leyva [email protected] Charlie Beckett [email protected] 1
London School of Economics and Political Science, London, UK
Department of Media and Communications, London School of Economics and Political Science, London, UK
2
Consequently, governments and media companies are developing high-tech algorithmic counter-measures to mitigate the spread of political DFN campaigns (Nelson and Taneja 2018). For example, Facebook and Twitter have purported that they have enhanced their machine learning protocols to better detect and remove DFN accounts and postings (Haciyakupoglu et al. 2018). However, these concerns and pre-emptive responses are currently mostly grounded on speculation rather t
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