Assessing (In)accuracy and Biases in Self-reported Measures of Exposure to Disagreement: Evidence from Linkage Analysis Using Digital Trace Data. Issue 3 (3rd July 2021) - Song, Hyunjin
ABSTRACT: Citizen's exposure to disagreement – whether intentional or incidental – is a central concept in communication research, yet the precise degree to which citizens are exposed to opposing views online and the antecedents to this phenomenon continue to be debated. Despite the theoretical importance of this question, empirical assessments of cross-cutting exposure, especially those involving online settings, are largely based on individuals' perception of their own behavior. Therefore, we know little regarding response bias in self-reports of cross-cutting exposure online. Combining digital trace data with a panel survey, we observe overreporting of self-reported online cross-cutting exposure. We then demonstrate that self-reported exposure to disagreement is retrospectively conditioned by the perception of the opinion climate in a given context. Finally, using Monte Carlo simulations, we examine the consequences of relying on (potentially imperfect) self-reported measures.