Views provided by UsageCounts
doi: 10.1073/pnas.2115900119 , 10.5281/zenodo.6915203 , 10.5281/zenodo.6915661 , 10.5281/zenodo.6915202 , 10.5281/zenodo.6915662
pmid: 35972960
pmc: PMC9407668
handle: 10161/28566
doi: 10.1073/pnas.2115900119 , 10.5281/zenodo.6915203 , 10.5281/zenodo.6915661 , 10.5281/zenodo.6915202 , 10.5281/zenodo.6915662
pmid: 35972960
pmc: PMC9407668
handle: 10161/28566
Following the 2020 general election, Republican elected officials, including then-President Donald Trump, promoted conspiracy theories claiming that Joe Biden’s close victory in Georgia was fraudulent. Such conspiratorial claims could implicate participation in the Georgia Senate runoff election in different ways—signaling that voting doesn’t matter, distracting from ongoing campaigns, stoking political anger at out-partisans, or providing rationalizations for (lack of) enthusiasm for voting during a transfer of power. Here, we evaluate the possibility of any on-average relationship with turnout by combining behavioral measures of engagement with election conspiracies online and administrative data on voter turnout for 40,000 Twitter users registered to vote in Georgia. We find small, limited associations. Liking or sharing messages opposed to conspiracy theories was associated with higher turnout than expected in the runoff election, and those who liked or shared tweets promoting fraud-related conspiracy theories were slightly less likely to vote.
Georgia, 550, Communication, Fraud, Politics, Social Sciences, Humans, Longitudinal Studies
Georgia, 550, Communication, Fraud, Politics, Social Sciences, Humans, Longitudinal Studies
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 28 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
| views | 6 |

Views provided by UsageCounts