Web Mining for Estimating Regulatory Blockchain Readiness

TL;DR

This paper presents a computational web mining model that quantitatively estimates countries' regulatory stance on cryptocurrencies, validated through experiments and enhanced by clustering analysis for better insights.

Contribution

It introduces a novel web mining-based model for assessing regulatory readiness of countries regarding cryptocurrencies, combining quantitative estimation with unsupervised learning.

Findings

High accuracy in regulatory stance estimation

Effective use of clustering for analysis

Validated approach with experimental results

Abstract

The regulatory framework of cryptocurrencies (and, in general, blockchain tokens) is of paramount importance. This framework drives nearly all key decisions in the respective business areas. In this work, a computational model is proposed for quantitatively estimating the regulatory stance of countries with respect to cryptocurrencies. This is conducted via web mining utilizing web search engines. The proposed model is experimentally validated. In addition, unsupervised learning (clustering) is applied for better analyzing the automatically derived estimations. Overall, very good performance is achieved by the proposed algorithmic approach.