TikTok Restores the Supply-Side Signal for Allergic Rhinitis Nowcasting in England: A Benchmark against Google Trends and Legacy Twitter

Twitter’s post-2023 access changes weakened supply-side infodemiology. We test whether TikTok restores that pillar for allergic rhinitis (AR) nowcasting in England. Using weekly incident AR consultations from the RCGP Research and Surveillance Centre as the clinical target, with MASK-Air® diaries and aerobiological pollen as covariates, we assemble a UK-located TikTok activity series via the Academic Research API and benchmark it against Google Trends and a legacy Twitter archive. A fixed-origin 2020–2024 design evaluates horizons h=0–4 with SNaive, SARIMAX, Elastic Net, Random Forest, and XGBoost. TikTok aligns with incidence at lag 0–1, whereas birch pollen leads by about six weeks. Replacing Twitter with TikTok (GT+TF→ GT+TK) reduces mean absolute error by about 20% for Elastic Net, 12% for SARIMAX, and 3–6% for tree models; RMSE reductions are smaller for Elastic Net/SARIMAX and comparable for tree models. Gains concentrate at nowcasting and attenuate with horizon, and some models do not beat the seasonal naive baseline at longer horizons. Operationally, TikTok’s Academic Research API enables scheduled, zero-cost weekly ingestion, while live Twitter ingestion is currently impracticable. We recommend pairing Google Trends with TikTok for operational nowcasting of AR in England, treating non-clinical streams as UK/GB proxies for the English clinical target.