Semantic Entropy and Tariff Policy Uncertainty Discourse on Reddit: Integrating Message-Level and Network-Level Optimal Information Theory

This study examines how structural linguistic novelty (bigram-based semantic entropy) and explicit uncertainty markers co-vary and predict market volatility in Reddit discussions of U.S. tariff policy. Analyzing 101 days of Reddit posts (February–May 2025), we computed daily aggregate entropy scores and uncertainty term frequencies, evaluating their relationship with the CBOE VIX index using 1–7 day lags. Bigram entropy moderately correlated with uncertainty word counts (r=0.47,p<0.001) and strongly with post volume (r=0.98,p<0.001). A seven-day ahead model demonstrated the highest explanatory power in multivariate regressions controlling for volume, uncertainty language, fear words, and sentiment (R2=0.42). Higher logentropy robustly predicted increased future VIX (β=146.14,p<0.001), while loguncertainty words also contributed positively (β=20.76,p<0.001). Fear terms and VADER sentiment were nonsignificant. These findings extend Danowski’s Optimal Information Theory (OIT), showing that message-level novelty (entropy) and redundancy (uncertainty cues) co-regulate in online discourse and differentially influence market responses over a week-long horizon. Policy implications include monitoring semantic entropy as an earlywarning indicator for market instability and pairing novel announcements with clear guidance to mitigate volatility. Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article yet reasonably common within the subject discipline.)