The impact of help-seeking text signals on social support receipt in social media during the COVID-19 outbreak: a cross-sectional study based on signaling theory

Abstract The COVID-19 pandemic has significantly increased reliance on social media for help-seeking during public health crises. Grounded in signaling theory, this study investigated how informational and emotional cues in social media help-seeking posts affect the receipt of online social support. We conducted a cross-sectional analysis of 807 help-seeking posts and 59,244 corresponding comments from the “COVID-19 Patients Help-Seeking Dialog” subforum on Sina Weibo, collected between January 29 and March 24, 2020. Using computational methods, while controlling for user identity type and intensity of social media use, the effects of readability, integrity, and emotional intensity of help-seeking posts on the receipt of social support by help seekers were analyzed. Our findings revealed three key insights: First, integrity positively predicts informational support receipt (β = 0.112, p < 0.05), suggesting that comprehensive problem descriptions enhance response quality. Second, emotional intensity negatively correlates with informational support (β = −0.132, p < 0.05), indicating that highly emotional appeals may reduce practical assistance. Third, lower readability predicts greater emotional support (β = −0.196, p < 0.001), potentially reflecting heightened empathy for complex medical narratives during crises. These results advance signaling theory by demonstrating how different textual cues affect social support outcomes in emergency contexts. Practically, the study provides evidence-based guidelines for: (1) help-seekers to optimize post composition, (2) platforms to design better crisis communication tools, and (3) policymakers to improve digital emergency response systems. The research underscores the need for tailored signaling strategies during public health emergencies when both informational accuracy and emotional support are critical.