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SoChainDB: A Database for Storing and Retrieving Blockchain-Powered Social Network Data

Published: 07 July 2022 Publication History

Abstract

Social networks have become an inseparable part of human activities. Most existing social networks follow a centralized system model, which despite storing valuable information of users, arise many critical concerns such as content ownership and over-commercialization. Recently, decentralized social networks, built primarily on blockchain technology, have been proposed as a substitution to eliminate these concerns. Since decentralized architectures are mature enough to be on par with the centralized ones, decentralized social networks are becoming more and more popular. Decentralized social networks can offer both common options like writing posts and comments and more advanced options such as reward systems and voting mechanisms. They provide rich eco-systems for the influencers to interact with their followers and other users via staking systems based on cryptocurrency tokens. The vast and valuable data of the decentralized social networks open several new directions for the research community to extend human behavior knowledge. However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss some interesting applications, e.g., game, non-fungible tokens market built upon Hive. It is worth mentioning that our framework is well-adaptable to other blockchain social networks with minimal modification. SoChainDB is publicly accessible at http://sochaindb.com and the dataset is available under the CC BY-SA 4.0 license.

Supplementary Material

MP4 File (SIGIR22-rs1571.mp4)
We propose the SoChainDB framework that facilitates obtaining data from blockchain-powered social networks since accessing and collecting data from these social networks is not easy. It often requires in-depth blockchain knowledge, which is not the focus of computer science and social science researchers. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss exciting applications, e.g., blockchain games extracted from the decentralized network by SoChainDB. Besides, the rich and valuable data from decentralized social networks also opens up several new directions for the research community to advance knowledge about human behavior. SoChainDB is publicly accessible at http://sochaindb.com, and the dataset is available under the CC BY-SA 4.0 license.

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  • (2024)Anomaly Behavior Analysis for Blockchain Social Networks Using Heterogeneous Graph Neural NetworksIntelligence of Things: Technologies and Applications10.1007/978-3-031-75596-5_24(259-268)Online publication date: 24-Dec-2024
  • (2023)How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting GovernanceProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3623171(150-164)Online publication date: 15-Nov-2023
  • (2023)Altruistic and Profit-oriented: Making Sense of Roles in Web3 Community from Airdrop PerspectiveProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581173(1-16)Online publication date: 19-Apr-2023
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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2022

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Author Tags

  1. blockchain
  2. database
  3. datasets
  4. decentralized applications
  5. decentralized social networks
  6. network analysis
  7. social networks

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  • European Union?s Horizon 2020 research and innovation program

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Cited By

View all
  • (2024)Anomaly Behavior Analysis for Blockchain Social Networks Using Heterogeneous Graph Neural NetworksIntelligence of Things: Technologies and Applications10.1007/978-3-031-75596-5_24(259-268)Online publication date: 24-Dec-2024
  • (2023)How Hard is Takeover in DPoS Blockchains? Understanding the Security of Coin-based Voting GovernanceProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3623171(150-164)Online publication date: 15-Nov-2023
  • (2023)Altruistic and Profit-oriented: Making Sense of Roles in Web3 Community from Airdrop PerspectiveProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581173(1-16)Online publication date: 19-Apr-2023
  • (2023)Non-Fungible Token (NFT) Games: A Literature Review2023 International Conference On Cyber Management And Engineering (CyMaEn)10.1109/CyMaEn57228.2023.10050961(251-254)Online publication date: 26-Jan-2023
  • (2022)Recent Advances of Blockchain and Its ApplicationsJournal of Social Computing10.23919/JSC.2022.00163:4(363-394)Online publication date: Dec-2022

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