  Participants in political discourse employ rhetorical strategies -- such as
hedging, attributions, or denials -- to display varying degrees of belief
commitments to claims proposed by themselves or others. Traditionally,
political scientists have studied these epistemic phenomena through
labor-intensive manual content analysis. We propose to help automate such work
through epistemic stance prediction, drawn from research in computational
semantics, to distinguish at the clausal level what is asserted, denied, or
only ambivalently suggested by the author or other mentioned entities (belief
holders). We first develop a simple RoBERTa-based model for multi-source stance
predictions that outperforms more complex state-of-the-art modeling. Then we
demonstrate its novel application to political science by conducting a
large-scale analysis of the Mass Market Manifestos corpus of U.S. political
opinion books, where we characterize trends in cited belief holders --
respected allies and opposed bogeymen -- across U.S. political ideologies.
