Temporal Dynamics of Influenza-Associated Anxiety Symptom Linguistic Markers on Weibo (2023–2024): A Study of Incidence Correlation, Diurnal Patterns, and Time-Series Trends (Preprint)

BACKGROUND Influenza seasons may intensify anxiety, a core affective symptom. Social media can surface population-level mental-health signals in real time. OBJECTIVE To characterize diurnal and full-season dynamics of anxiety-related language during the 2023–2024 influenza season in China and its association with influenza activity. METHODS We retrieved Sina Weibo posts in February 2025 covering 4 Sep 2023–28 Apr 2024. Posts containing DSM-based anxiety terms were cleaned and de-duplicated (N=169,728 → 10,6440). We firstly linked weekly influenza incidence with anxiety-related postings.Then we plotted diurnal patterns by epidemiologic phase, normalized symptom counts by platform activity, and modeled longitudinal symptom trajectories using ARIMA time-series models. RESULTS Anxiety-related posts closely followed influenza activity, surging during the outbreak and peak phases and remaining elevated even after influenza declined. Spearman correlation showed significant associations for irritability (r=–0.393, p = 0.026) and restlessness or feeling keyed up or on edge (r=–0.376, p = 0.034). Diurnal patterns shifted across stages, showing mild early–evening variation during the outbreak, clear morning peaks with secondary afternoon and evening rises during prevalence and decline, and morning– afternoon concentration in the end stage. ARIMA time-series analysis revealed that irritability and being easily fatigued consistently dominated the discussions, whereas others remained at relatively low levels. The 30-day forecast indicated overall stability, with being easily fatigued showing a slight increase. CONCLUSIONS This study demonstrates how social media can capture diurnal and seasonal fluctuations of anxiety symptoms linked to influenza incidence, advancing understanding of affective dynamics in population health. CLINICALTRIAL None