  Multifaceted ideology detection (MID) aims to detect the ideological leanings
of texts towards multiple facets. Previous studies on ideology detection mainly
focus on one generic facet and ignore label semantics and explanatory
descriptions of ideologies, which are a kind of instructive information and
reveal the specific concepts of ideologies. In this paper, we develop a novel
concept semantics-enhanced framework for the MID task. Specifically, we propose
a bidirectional iterative concept flow (BICo) method to encode multifaceted
ideologies. BICo enables the concepts to flow across levels of the schema tree
and enriches concept representations with multi-granularity semantics.
Furthermore, we explore concept attentive matching and concept-guided
contrastive learning strategies to guide the model to capture ideology features
with the learned concept semantics. Extensive experiments on the benchmark
dataset show that our approach achieves state-of-the-art performance in MID,
including in the cross-topic scenario.
