Combining interventions to reduce the spread of viral misinformation.

ABSTRACT: Misinformation online poses a range of threats, from subverting democratic processes to undermining public health measures. Proposed solutions range from encouraging more selective sharing by individuals to removing false content and accounts that create or promote it. Here we provide a framework to evaluate interventions aimed at reducing viral misinformation online both in isolation and when used in combination. We begin by deriving a generative model of viral misinformation spread, inspired by research on infectious disease. By applying this model to a large corpus (10.5 million tweets) of misinformation events that occurred during the 2020 US election, we reveal that commonly proposed interventions are unlikely to be effective in isolation. However, our framework demonstrates that a co

SUBMITTER: Bak-Coleman JB 

PROVIDER: S-EPMC9584817 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature