Testing and unpacking the effects of digital fake news: on presidential candidate evaluations and voter support

Autor: Charlie Beckett, Rodolfo Leyva
Rok vydání: 2020
Předmět:
Zdroj: AI & SOCIETY. 35:969-980
ISSN: 1435-5655
0951-5666
DOI: 10.1007/s00146-020-00980-6
Popis: 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.
Databáze: OpenAIRE