This study examines how citizens’ discursive strategies influence street-level bureaucrats’ (SLBs) prioritization decisions. Drawing on the research regarding deservingness heuristics, citizen voice behavior, and citizen resistance strategies, we conceptualize discursive strategies as showing deservingness, threatening to petition higher-level authorities, and threatening to engage in self-harm. Using a dataset of 254,257 transcribed interactions between citizens and hotline operators recorded on the government service hotline system (similar to the US 311 system) across 2019 in China, we identify discursive strategies in citizen complaints through a supervised machine learning (SML) algorithm. Our analysis shows that SLBs are significantly more likely to prioritize responding to citizens’ complaints that include the discursive cues of showing deservingness, petitioning higher-level authorities, and engaging in self-harm, with deservingness exerting the weakest effect. These findings contribute to our understanding of SLBs’ prioritization decisions in government-citizen interactions.
