SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform

TL;DR

This paper introduces SteemOps, a comprehensive dataset of over 900 million blockchain operations from Steemit, enabling advanced research into blockchain-based social media platforms.

Contribution

We collected and organized a large-scale dataset of Steemit operations, creating SteemOps to support future research in blockchain social media analysis.

Findings

Over 38 million blocks collected from 2016 to 2019

900 million operations organized into three key datasets

Facilitates research on blockchain social media dynamics

Abstract

Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.