crowdsourcing
Definition
Crowdsourcing refers to the act of obtaining services, ideas, or content by soliciting contributions from a group, particularly from an online community, rather than from traditional or expert suppliers. In the context of misinformation research, it describes systems in which laypeople share responsibility for evaluating and flagging misleading content on social media platforms. Aggregate judgments from politically heterogeneous groups of non-experts have been shown to align closely with professional fact-checkers, and even extreme partisans reduce misinformation sharing in proportion to the number of community members who rate a post as misleading. The success of such interventions depends on three psychological factors: the degree of cognitive dissonance a fact-check creates with prior beliefs, the level of trust users place in the fact-checking sources, and the size of the crowd providing the ratings.
Sources: Pretus et al. (2024)
Related Terms
- misinformation (1 shared article)
- fact-checking (1 shared article)
- trust (1 shared article)
- network analysis (1 shared article)
Applications
Crowdsourcing and Partisan Misinformation
Crowdsourcing interventions offer an additional layer of content moderation specifically designed to address the limits of conventional debunking among politically polarized users. Far-right extreme partisans, who spread the largest share of online misinformation, are among the least responsive to traditional correction efforts, yet collective accuracy judgments have been shown to reduce their misinformation sharing. The effectiveness of this approach is conditioned on balancing trust, cognitive dissonance, and crowd size, three factors that are frequently in tension within ideologically segregated social media environments.
Sources: Pretus et al. (2024)
Crowdsourcing and Political Heterogeneity
The accuracy of crowdsourced misinformation judgments improves markedly when the crowd is politically heterogeneous rather than ideologically uniform. Politically balanced groups produce collective estimates that better discriminate between true and false statements, whereas homogeneous groups exhibit increased political bias following social influence. This relationship means that interventions designed to scale crowdsourcing must actively account for crowd composition, not merely crowd size.
Sources: Pretus et al. (2024)
Crowdsourcing and Echo Chambers
The structural design of crowdsourcing interventions on social media determines whether they reinforce or counteract ideological echo chambers. Exposing users only to fact-checks from accounts they already follow risks amplifying ingroup bias, while restricting exposure exclusively to outgroup fact-checkers reduces trust in the ratings. A proposed solution involves network analysis methods to connect users with communities that are ideologically adjacent but not identical, described as a two-steps-away approach.
Sources: Pretus et al. (2024)
