Numerous indicators suggest that democracy is under threat worldwide, with misinformation cited as a major concern. Misinformation is troubling not only because it lingers in memory even if people know it has been corrected, but also because under certain circumstances people come to view and value inaccuracy as a signal of “authenticity.” Within a populist logic, blatant lies violate the “establishment” norm of accuracy, thereby signaling the “authenticity” of a champion of “the people”. A lying politician may be considered “honest” because they are authentically “speaking their mind”. Such belief-speaking is one component of a tripartite model of honesty developed by the funders (see figure) and involves only the speaker’s beliefs and feelings, irrespective of factual accuracy. For a democracy, “belief-speaking” is problematic because it allows leaders to “honestly” speak beliefs without seeking common ground based on the actual state of the world. We therefore urgently need a better understanding of what is considered politically “honest”, and under what circumstances. Within the tripartite model, this quest requires exploration of the balance between the three components. We pursue this quest computationally, using largescale data analysis and text modeling of corpora.
|Effective start/end date||1/10/21 → 30/09/23|
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