Reddit's 'pulse' on US inflation: forecasting with large language models

Andrea Del Monaco (), Luigi Longo (), Juri Marcucci () and Irene Tafani ()
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Andrea Del Monaco: Bank of Italy
Luigi Longo: JRC - European Commission
Juri Marcucci: Bank of Italy
Irene Tafani: IMT School Lucca

No 1028, Questioni di Economia e Finanza (Occasional Papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: We show that large language models (LLMs) can transform Reddit discussions into timely predictors of US inflation. Using inflation-related submissions and time-local comments from major economics-focused subreddits, we construct monthly narrative indicators that capture perceived price dynamics. Signals are generated by fine-tuning pre-trained models (BERT-, Qwen-, LLaMA-, and Gemma-type architectures) for labels produced by human annotators and ChatGPT and benchmarked against a non-fine-tuned LLaMA-70B model. Forecasting and nowcasting are implemented in pseudo-real time with strictly backward-looking transformations, recursive expanding windows, and explicit data-availability constraints. In a recursive pseudo out-of-sample evaluation with horizons up to 18 months, Reddit-LLM models and MSE-weighted forecast combinations improve point and density forecasts of headline CPI and core PCE relative to standard benchmarks, including autoregressive models augmented with Michigan survey expectations and inflation swaps. In real-time nowcasting, Reddit signals constructed using information available early in the month improve nowcasts and perform competitively with the Cleveland Fed Inflation Nowcast. Importantly, much of the predictive content can be captured with fine-tuned small language models (SLMs), which often deliver performances close to those of much larger LLMs at a fraction of the computational cost, supporting scalable and resource-efficient deployment.

Keywords: economic forecasting; social media; Reddit; inflation; text mining; text-as-data; text analysis; natural language processing; sentiment analysis; Big Data; large language models; ChatGPT; generative artificial intelligence (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 E31 (search for similar items in EconPapers)
Date: 2026-06
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