Incentivized Blockchain-based Social Media Platforms: A Case Study of Steemit
Chao Li, Balaji Palanisamy

TL;DR
This study empirically evaluates Steemit, a blockchain-based social media platform, revealing low decentralization levels and significant misuse of its cryptocurrency reward system, highlighting challenges in its design and consensus protocol effectiveness.
Contribution
The paper provides the first comprehensive analysis of Steemit's decentralization, reward system misuse, and operational challenges, offering insights into blockchain social media platforms.
Findings
Decentralization in Steemit is significantly lower than ideal levels.
Over 16% of cryptocurrency transfers are suspected to be bot-related.
Existence of a supply network for bot accounts suggests reward system abuse.
Abstract
This paper presents an empirical analysis of Steemit, a key representative of the emerging incentivized social media platforms over Blockchains, to understand and evaluate the actual level of decentralization and the practical effects of cryptocurrency-driven reward system in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol. Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. In Steemit,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBlockchain Technology Applications and Security · Spam and Phishing Detection · Mobile Crowdsensing and Crowdsourcing
