Hype Has Worth: Attention, Sentiment, and NFT Valuation in Major Ethereum Collections

TL;DR

This study investigates how community attention and sentiment influence NFT prices in major Ethereum collections, revealing that sustained attention and sentiment significantly impact valuation, with short-term negativity sometimes correlating with higher prices.

Contribution

It introduces a comprehensive analysis linking discourse and visual traits to NFT valuation, employing novel sentiment and attention measures within a mixed-effects modeling framework.

Findings

Sustained collection-level attention correlates with higher NFT prices.

Negative sentiment over short horizons is associated with increased prices.

Visual heterogeneity is controlled using PCA-based image trait indices.

Abstract

Do online narratives leave a measurable imprint on prices in markets for digital or cultural goods? This paper evaluates how community attention and sentiment relate to valuation in major Ethereum NFT collections after accounting for time effects, market-wide conditions, and persistent visual heterogeneity. Transaction data for large generative collections are merged with Reddit-based discourse measures available for 25 collections, covering 87{,}696 secondary-market sales from January 2021 through March 2025. Visual differences are absorbed by a transparent, within-collection standardized index built from explicit image traits and aggregated via PCA. Discourse is summarized at the collection-by-bin level using discussion intensity and lexicon-based tone measures, with smoothing to reduce noise when text volume is sparse. A mixed-effects specification with a Mundlak within--between decomposition separates persistent cross-collection differences from within-collection fluctuations. Valuations align most strongly with sustained collection-level attention and sentiment environments; within collections, short-horizon negativity is consistently associated with higher prices, and attention is most informative when measured as cumulative engagement over multiple prior windows.