Learning from Incidental Exposure to Political Information in Online Environments

Abstract

Abstract This article aims to explain learning outcomes of incidental exposure (IE) to political information in online environments. Drawing on the Political Incidental News Exposure Model, we predict learning outcomes by distinguishing between first-level (i.e., scanning of incidentally encountered information) and second-level IE (i.e., effortful processing of incidentally encountered information appraised as relevant). Furthermore, we conceptualize intention-based IE (i.e., while looking for non-political content) and topic-based IE (i.e., while looking for different political content). In a 2x2 experiment (N = 290), we manipulated respondents’ initial processing goal (i.e., political or non-political information) and low (i.e., first-level IE) versus high (i.e., second-level IE) relevance of the incidentally encountered information. Results show stronger learning effects for second level than for first-level IE. Learning effects do not differ between topic-based and intention-based IE, but second-level IE decreases learning related to the initial processing goal. Theoretical implications are discussed.