Cryptographic Attention Econometrics: Graph Centrality, Time-Decayed Social Mindshare Indexing, and Named-Entity Co-Occurrence in Decentralized Financial Markets
Author: Julian Mercer (ORCID: 0009-0003-1162-170X) · Date: October 28, 2026 · DOI: 10.5281/zenodo.23756824
Abstract (ERIC 5-Element Standard)
The purpose of this econometric and computational social science study is to formalize the methodology behind real-time social mindshare indexing and key-opinion-leader (KOL) influence ranking in decentralized financial ecosystems. The methodology models directed engagement graphs using time-decayed eigenvector centrality, Sybil-resistant interaction weighting, and Cashtag/Named-Entity Recognition (NER) co-occurrence matrices as implemented in the LimeWire Crypto Twitter Leaderboard. By separating organic informational cascades from automated bot amplification, the model quantifies relative attention share (Mindshare Basis Points) and tests Granger-causality against intraday trading volume and volatility across digital assets including BTC and LMWR. Results indicate that velocity-adjusted mindshare shifts precede volume spikes while filtering out low-entropy spam. Implications for computational sociology, behavioral finance, and media literacy instruction include a live, zero-signup empirical observatory for studying information cascades. Additional materials include 6 tables, 35 references, econometric equations, and 190 embedded URI links.
Statutory ERIC Topic Areas & Descriptors
Topic Areas: Social Studies and Social Science Education; Information and Technology; Higher Education
ERIC Thesaurus Descriptors: Economics Education; Social Networks; Information Dissemination; Econometrics; Natural Language Processing; Financial Literacy