Altruistic and Profit-oriented: Making Sense of Roles in Web3 Community from Airdrop Perspective

Title of the Paper

Altruistic and Profit-oriented: Making Sense of Roles in Web3 Community from Airdrop Perspective

Paper Information

• Subject Area: Blockchain Technology and Web3 Community Management
• Keywords: Decentralized Community, Airdrop, Network Analysis, Unsupervised Learning, Ethereum, Blockchain Governance, Network Transaction Patterns, Web3, Community Participation, Smart Contracts

Research Background and Issues

• Problems or Challenges Identified by the Authors:

  1. There is ongoing debate on how to incentivize users through airdrops and fairly allocate capital and governance power in decentralized communities.
  2. Anonymity makes it difficult to analyze user behavior through interviews or surveys.
  3. The effectiveness of airdrop incentive mechanisms in achieving decentralized governance among community members is unclear, and they may also trigger speculative behavior, destabilizing the community.

• Importance of the Problem:
  Decentralized communities are a vital component of blockchain technology and Web3 societal applications. Their governance and member behavior are directly linked to their long-term sustainability.

• Research Motivation and Related Work:

  1. Governance and resource allocation in decentralized environments are more complex than in traditional internet communities.
  2. Airdrops, as a mainstream incentive method, have not been sufficiently studied to evaluate their efficiency and issues.
  3. Existing research mostly focuses on transaction record analysis, lacking in-depth analysis of Web3 community user characteristics.

Solution

• Proposed Method or Solution by the Authors:

  1. A data-driven study analyzing the behavior of airdrop users in the ParaSwap community to propose a role classification method.
  2. Using unsupervised learning to cluster airdrop user behaviors and define community member roles.
  3. Employing network analysis to evaluate the decentralized characteristics of ecosystem participants, including airdrop hunters and community members.

• Innovative Aspects:

  1. Combining airdrop behavior with the identification of community member roles to explore the relationship between "altruistic" and "profit-driven" behaviors.
  2. Proposing a new role classification method and offering optimization suggestions for addressing airdrop hunting behavior and policies.

• Implementation Steps and Key Technologies:

  1. Data collection and preprocessing: Obtaining ParaSwap transaction records from the blockchain to construct user transaction behavior profiles.
  2. Using clustering algorithms to analyze user behavior and classify users based on specific metrics.
  3. Network structure analysis: Revealing potential airdrop hunting behaviors by analyzing transaction graphs.

Research Outcomes

• Specific Findings:

  1. Proposed a role classification method for community members based on unsupervised clustering, categorizing users into five roles: speculators, diamond holders, airdrop hunters, diversified participants, and active buyers.
  2. Identified structural patterns and methods of airdrop hunting behavior through network analysis, such as "chain transactions" and "sunflower transactions."
  3. Found that tiered airdrop reward policies encourage members to engage in long-term beneficial community behaviors.

• Advantages Compared to Existing Solutions:

  1. Provides a deeper perspective on human behavior in blockchain community governance, going beyond mere transaction record analysis.
  2. Proposes optimized airdrop reward policies and role identification methods to reduce airdrop hunting behavior.
  3. Combines network analysis with decentralized community governance, offering actionable data support.

• Experimental or Evaluation Results:

  1. In the ParaSwap community, 86.39% of initial members exited the community within six months after the airdrop.
  2. Most airdrop hunters successfully bypassed screening mechanisms through complex network transaction patterns, concentrating governance power.
  3. Tiered reward airdrop policies incentivize users to engage in more positive community behaviors.

• Limitations and Future Directions:

  1. Airdrop hunting behavior may become more covert, and current methods have limited ability to track transactions involving centralized exchanges and mixing services.
  2. Conducting in-depth interviews and behavioral surveys with anonymous users is challenging, requiring more education and user cognition research.
  3. Future work could explore phased airdrop rewards combined with behavioral dynamics to enhance long-term community participation. Additionally, research could investigate how blockchain communities can inspire altruistic behavior from a user experience perspective.