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

Tian MinThe Chinese University of Hong Kong, Shenzhen
Shenzhen, China
sizhengfan@link.cuhk.edu.cn

The Chinese University of Hong Kong, Shenzhen
Shenzhen, China
tianmin@link.cuhk.edu.cn

Xiao Wu

Wei Cai∗White Matrix Inc.
Nanjing, China
wuxiao@whitematrix.io

The Chinese University of Hong Kong, Shenzhen
Shenzhen, China
caiwei@cuhk.edu.cn

ABSTRACT

1Regardless of which community, incentivizing users is a necessity
for well-sustainable operations. In the blockchain-backed Web3
communities, known for their transparency and security, airdrop
serves as a widespread incentive mechanism for allocating capitaland power. However, it remains a controversy on how to justify
airdrop to incentive and empower the decentralized governance.
In this paper, we use ParaSwap as an example to propose a role
taxonomy methodology through a data-driven study to understandthe characteristic of community members and the efectivenessof airdrop. We fnd that users receive more rewards tend to takepositive actions towards the community. We summarize several
arbitrage patterns and confrm the current detection is not sufcientin screening out airdrop hunters. In conjunction with the results,
we discuss from the aspects of interaction, fnancialization, andsystem design to conclude the challenges and possible research
directions for decentralized communities.

Since the early days, many research eforts have been on evaluating
a community, trying to model its members and quantify metrics
such as reciprocity and sustainability. As technology has evolved,
communities have come to refer to more than just real people, but
can also be used to represent groups of users on the Internet thatshare specifc characteristics. With the development of blockchain
technology and the popularization of decentralized applications,the emergence of the so-called Web3 has brought the communities
built on this particular background into our research horizon. The
anonymity of blockchain prevents us from conducting user interviews and questionnaire research, but its unique transparent natureallows us to infer user behavior through publicly available datasets.
Compared to centralized communities, decentralized communities of Web3 introduce more challenges, including ways to rationalize the allocation of power and resources or ensure that individuals do not dictate the will of the community. The disappearing
central governance reforms itself in various mechanisms of othercomposite appearances, such as democratic voting on community
development using the governance token. A real-world example
is Juno Network, published Juno Proposal 16 on March 11�ℎ , 2022,
calling the community to whether the wallet of a whale1 shouldhave a large chunk of its token removed, like court enforcementof property. The motivation behind this was when Juno Network
rewarded its early supporters with governance token, known as an
airdrop2 , the whale obtained 2.5 million tokens through multiple
addresses, which accounted for 9.6% of the total amount and could
bring a huge potential security risk to the community. The proposal
is highly controversial because it marks the frst strong-arm tactics
of on-chain governance. It is high time for decentralized communities to discuss and rethink the essence of their own creation. On the
one hand, it raises the community’s concern about the ownership of
on-chain assets. On the other hand, the potential harm and future
development of the airdrop have been widely discussed.
Unlike a community in the traditional context, a decentralized
community is much looser in scope. Joining and exiting a decentralized community often simply means whether you are a stakeholder
of a certain type of token: holding assets issued by a specifc protocol implies corresponding rights and obligations. The protocols,

CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing.

KEYWORDS
Decentralized community, Airdrop, Network analysis, Unsupervised learning
ACM Reference Format:
Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai. 2023. Altruistic and Proftoriented: Making Sense of Roles in Web3 Community from Airdrop Perspective. In Proceedings of the 2023 CHI Conference on Human Factors in
Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM,
New York, NY, USA, 16 pages. https://doi.org/10.1145/3544548.3581173∗ Wei Cai is the corresponding author.

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor proft or commercial advantage and that copies bear this notice and the full citationon the frst page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specifc permission
and/or a fee. Request permissions from permissions@acm.org.
CHI ’23, April 23–28, 2023, Hamburg, Germany© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-9421-5/23/04. . . $15.00
https://doi.org/10.1145/3544548.3581173

INTRODUCTION

1 Means an address that holds a large amount of capital.
2 An airdrop is an unsolicited distribution of cryptocurrency or Non Fungible Tokens(NFTs), usually without conditions, to multiple wallet addresses.

CHI ’23, April 23–28, 2023, Hamburg, Germany

substantially, are the large number of automatically executed programs not controlled by individuals or organizations, also called
smart contracts, tasked with the responsibility of regulating thecommunity. The regulation or resolution for the decentralized community is valid only if most of its members support the decision
through fungible tokens (FTs) or NFTs voting. These governancetokens grant their holders voting rights like a shareholder meetingof listed companies. Members and potential participants are usually
motivated to contribute to the community, especially in the earlystages. For example, some decentralized applications (DApps), suchas Uniswap, dYdX, etc., issue airdrops to early supporters for theircontributions. Some NFT projects ofer whitelist quota3 for promotion services. The simple logic behind this is the contribution forthe privilege, which indicate that users who contribute more are
more likely to be competent to the governance and should be given
more voting power.
However, due to the fnancial nature inherent in blockchain technology the blockchain, as of now, all digital assets on the blockchain
network can be exchanged for real-world currency by various
means, which implies that from a gaming standpoint, decentralized
communities in Web3 are facing a more signifcant challenge interms of altruism than traditional Internet communities with open
source spirit. Among the early supporters eligible for airdrop, mostof them are motivated by fnancial proft or preferential access totokens with governance rights in order to obtain quick cash. Manyof them exploit the anonymity of blockchain, registering multiple
accounts and interacting with the DApp to maximize their profts,
which can not be taken as a positive factor for the long-term development, because once they acquire the tokens, exploiters tend
to sell the tokens as soon as possible, consequently making a dropof the asset price. These behaviors undermine the rights and theinterests of those enthusiasts who would like to hold the tokens in
the long run and proactively engage in the community.
To address these issues, many DApps have begun to distribute
governance tokens by designing stringent fltering mechanisms
where a typical example is ParaSwap4 . It is particularly strict inselecting eligible airdrop addresses: in the past two years, around
1.3 million addresses have interacted with this protocol, but only
19,999 of them were rewarded with PSP, ParaSwap’s governancetoken. The rigorous fltering approach caused widespread concern
across the Web3 community. Some early supporters criticized the
project for being screened out while they are actual users who
made a practical contribution. The distribution method is signifcant
because of its dual implication in terms of governance power andmonetary, and thus triggered intense debate on the efectiveness ofairdrops.
In this paper, we take ParaSwap as a representative example,
trying to evaluate the Web3 community and the efectiveness of
allocation principles through the analysis of eligible users’ behavior
and token transaction network. We collect PSP transactions of
20,148 addresses between November 15�ℎ , 2021 and April 13�ℎ ,2022, based on which we analyze their behavior before and after
the airdrop event and the allocation method from the following
3 A whitelist in the NFT world is a list of people who can get early and guaranteed

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

two aspects. From the macro aspect, we investigate the temporaldevelopment of the network properties, based on which we also
fnd a series of the unique structure of the interaction between
token holders through component analysis by comparing the token
network with the external transaction network. From the micro
aspect of individual addresses, we perform unsupervised clustering
on eligible airdrop addresses based on their transaction patterns.
We then compare the distribution diference in terms of the amountof capital and token holding duration among diferent clusters. Our
main contributions can be summarised as follows:• Token Network Analysis. We employ global network properties, such as reciprocity, degree assortativity coefcient,and attracting components, to detect signifcant changes andanomalies over PSP token networks. We decompose the network into components and discuss it in-depth with respect
to potential arbitrage behavior.
• Unsupervised Clustering. We utilize an unsupervised hierarchical clustering method to capture the behaviors of eligible
airdrop addresses after receiving rewards and divide theminto interpretable categories. Based on the cluster results,
we reveal that the threshold diferential allocation can perform a better allocation of governance power to long-term
contributors.
• Evaluation on Web3 Community. As a prerequisite for rational governance and creating a healthy online community, thesuitable incentive mechanism and power allocation play an
irreplaceable role. We point out that huge problems persist
behind the idealism of equality and democracy promoted byconcepts such as the Metaverse [16]. Combined with our data
fndings, we discuss the pros and cons of current approaches
and suggest that social experiments in blockchain and Web3
as vehicles may be instructive for real-world social research.
Finally, we proposed the challenges of the community engagement and governance as future directions and potential
improvements for the Web3 Community.

2 BACKGROUND
2.1 Ethereum and Smart Contract
Ethereum [55, 57] has a long history of development and was designed to address several limitations and challenges of the Bitcoin[41]. It provides the developers with a tightly integrated end-to-end
system for building software on a hitherto unexplored computeparadigm in the mainstream: a trustful object messaging compute
framework with smart contracts, which are scripts that run synchronously on multiple nodes of a distributed ledger without theneed of an external trusted authority [50, 60]. At the inception of
Ethereum, it followed the consensus mechanism5 of Bitcoin, called
Proof-of-Work (PoW), which requires network members to solve
an arbitrary mathematical puzzle. As PoW has the problem of wasting a lot of computing power with low performance and efciency,
blockchain platforms have started to adopt more efcient models, including Proof-of-Stake (PoS), Delegated Proof-of-Stake (DPoS), andProof-of-Authority (PoA) [24] as the technology evolves. Ethereum
upgraded from the PoW to PoS on September 15, 2022, so-called

access to mint during a specifc date.
4 ParaSwap is a decentralized middleware aggregator on the Ethereum blockchain that

5 Consensus mechanism refer to methodologies used to achieve consistent, trust, andofers the best prices across various DEXs.

security across a decentralized computer network.

Making Sense of Roles in Web3 Community from Airdrop Perspective

The Merge6 , which has dramatically improved its transaction persecond (TPS) and further facilitated the development of DApps.
There are three types of addresses on Ethereum: 1) Externally
Owned Address, so-called user address, operated by human users
with private keys; 2) Contract Address, namely the smart contracts,
governed by the internal contract code, acting as an autonomous
agent; 3) Prescribed Address is a kind of special addresses withvarious functionalities that similar to the prescribed strings in a
programming language.
Judging by the identity of the party who initiates the transaction,
we can divide the transaction types into two categories: external andinternal. External transactions are initiated by user addresses, andthey can be direct transactions between user addresses or function
calls to smart contracts. Internal transactions are initiated by smart
contracts that can transmit between themselves or can send tokens
to users, as in the case of airdrop contracts.

2.2

CHI ’23, April 23–28, 2023, Hamburg, Germany

2.3

ParaSwap’s Allocation Method

As more and more DApps are using airdrops to reward early adopterswith increasingly large amounts of money, a breed of people known
as "Airdrop Hunters", who create multiple wallet addresses and interact with the protocol to obtain multiple airdrops [35]. Their actions
will hinder the decentralization of the protocol. To cope with thissituation, DApps are constantly optimizing and adjusting their allocation policies. As shown in Figure 1, from fair allocation led
by Uniswap in the very beginning, to diferential allocation trend
formed by dYdX8 , to threshold diferential allocation represented
by ParaSwap, the difculty of getting airdrop is increasing. Thisis an important reason why we choose ParaSwap as the objectof our study, because it is not only the representative of airdrop
method at this stage, but its stringent flter is also the trend of future
development.

Airdrop in Web3

Prior to delving into the topic of governance in the realm of Web3, it
is imperative to have a basic understanding of the formation processof decentralized communities. This will facilitate a clearer appreciation of the crucial part played by airdrops in the enhancement ofcommunity empowerment and decentralization progression.
The empowerment of decentralized communities in Web3 is a
gradual and long-term process. Similar to public companies, DApp
start-up teams follow a defned process to publish their white paper,
which outlines the total number of tokens and their allocation, as
well as the timeline for distribution. Although the regulations mayvary from DApp to DApp, the format tends to be standardized.
The majority of DApps complete the distribution process withina few years, excluding the community vault and tokens retainedby the start-up team. The method of distribution is crucial to thesuccess of Web3 communities, as the absence of this process would
render the community an incomplete and unfulflled vision. To
ensure the success and sustainability of decentralized communities,
it is essential to have a well-defned and executed plan for token
distribution and community empowerment.
Airdrops, as a mainstream way of distributing tokens, are initiated by crypto projects to ofer their native tokens to current
or potential users in building a decentralized community. They
serve multiple purposes, being akin to virtual coupons that can bedeposited directly into a wallet. These coupons serve as a way to attract new customers or reward existing ones. The tokens that are apart of an airdrop are usually compliant with Ethereum Request forComments (ERC)7 standards and can function as a form of currency
or grant governance rights in the form of governance tokens. The
distribution of airdrops is determined by the level of participationin specifc events hosted by the blockchain platform, DApps, or
NFT purchases. In conclusion, airdrops play a vital role in fostering
the growth and involvement of decentralized communities and are,therefore, a crucial tool for any crypto project seeking to establish
a thriving decentralized community.

Figure 1: Development of Airdrop
To identify the hunters, ParaSwap frst fltered the addresses
with at least 50 transactions on the respective networks or havea minimum native token balance9 . Diferent from existing rules,
ParaSwap set a threshold of having at least six times of interactionsin the last six months. Besides, ParaSwap leveraged the common
patterns between hunters since they usually make external transactions between their accounts, which could flter addresses whichwere a part of a clique with size >5. User will be graded with theirlevel of activity to determine the reward tier10 .

3 RELATED WORK
3.1 Governance in Online Communities
Millions of communities gather in online spaces such as Twitter,
Reddit, Discord, and so on, which fll an essential part of our daily
lives and continue to shape an interconnected social scene diferently than ever before. However, behind the prosperity of online
communities, issues such as disinformation, sexual harassment,
infammatory and hate speech remain. Therefore, regulations areurgently needed and play an irreplaceable role in community governance. Today, community governance is mainly in the form ofa centralized model consisting of roles and permissions, wheregroups such as administrators and moderators have broad privileges over ordinary users. The regulators can be a specifc department of service provider, user autonomy [12], or hired personnel
for content review [11], to perform intervention or censorship suchas deplatforming [30], quarantining [13] with the help of artifcial intelligence and algorithms [29, 47]. Originating from Web 2.0
developed nearly twenty years ago, this centralized governance
8 https://dydx.exchange
9 ParaSwap set the minimum token balance to 0.028 ETH for Ethereum, 0.25 BNB for

6 https://ethereum.org/en/upgrades/merge/

BSC, 20 Matic for Polygon, and 0.9 AVAX for Avalanche C-Chain.

7 ERC is a document to write smart contracts on Ethereum. They describe rules that

10 https://medium.com/paraswap/whats-an-active-user-clarifying-psp-token-

Ethereum-based tokens must comply with.

distribution-fltering-logic-81df6096d410

CHI ’23, April 23–28, 2023, Hamburg, Germany

model is now used in software for almost all major online platforms.
Researchers have conducted an extensive and in-depth study on thegovernance aspects of online communities. For example, Manoel etal. [26] studied the level of activity, and ideological changes, when
toxic online communities on mainstream platforms faced moderation measures such as bans and migrated to other platforms. They
showed increases in signals associated with toxicity and radicalization, which justifes concerns that the reduction in activity may
come at the expense of a more toxic and radical community. Facedwith an endless chase between moderator and violator, researchers
are exploring the potential of blockchain technology to facilitatecommunity governance and democratize decision-making.
By autonomously executing smart contracts, DApps can provide
users with a variety of decentralized services, including crowdfunding, decentralized exchanges (DEXs), social networking, decentralized autonomous organizations (DAOs), and blockchain games.
They expand the possibility of governance and encode certain values that make top-down, authoritarian and, punitive governanceeasier to implement. DApps try to achieve a complete decentralization and direct democracy through issuing governance token,
that is, to establish a universal voting system where the directionof the project’s development is determined by the whole community. Proposals for voting can be community cooperation, using thecommunity vault, or changing the rule of a digital game. The governance under blockchain is challenging, and it is difcult to steer
a decentralized community and promote its development without
sacrifcing decentralization [17]. Techno-determinism seems morefavored at the expense of the complexity of the social organization.
This has partly contributed to the current chaotic public opinion
on blockchain. Proponents of centralization also argue that a traditional centralized authority is necessary for the current highly
fnancialized blockchain to emphasize the role of the state [5].
Although in the very recent past, blockchain-based media platforms (LBRY, CreativeChain, MediaChain, Blockphase) emerged toprovide services such as copyright protection [18], the emphasisof decentralized community governance is much diferent compared to the mainstream Web 2.0 community nowadays. Due to thedisparity in user volume and platform trafc, it is difcult for us
to identify social platforms similar to Twitter and Facebook from
Web3 and to draw a side-by-side comparison of their mechanism incontent regulation. However, this does not prevent us from exploring the characteristics of user behavior in existing decentralized
communities to pave the way for possible future emergence.

3.2

Study on Decentralized Communities

Since the inception of blockchain technology, developers and researchers have never stopped trying to defne or paint an ideal
picture of a decentralized community, also known as DAOs, interms of technology and social science benchmarks, but there does
not have a consensus. However, a comprehensive defnition madeby Luis Cuende is that “a DAO is an internet-native entity with nocentral management which is regulated by a set of automaticallyenforceable rules on a public blockchain, and whose goal is to takea life of its own and incentives people to achieve a shared common
mission” [17].

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

Lustig et al. [37] studied the sociotechnical imaginaries about
how people envision the possible and desirable future of decentralized autonomous systems and identifed three framings of autonomous technology as physical objects, as mathematical rules andas artifcial managers, which can inform the design and governanceof this novel concept. Faqir et al. [17], from a more specifc andpractical perspective, review the collective governance DAO providing platforms and their key features. At the same time, from the
aspect of system construction, DAO-Analyzer-like evaluation systems [4] that can visualize the activity and participation of DAO arealso being actively developed, thanks to the open-source nature ofblockchain data. At the same time, other critical and skeptical voices
are helping to correct the direction of the decentralized community.
Nabben et al. [40] suggested that reputation in blockchain systems
could become the new algorithmic authoritarianism if misused forsocial control and would conversely weaken the human factor inautonomy by employing ethnographic methods. Meijer et al. [38]provide a conceptualization of the consequences of blockchain systems that can imply the way of developing regulatory arrangements
and strategies that strike a balance between power and possibilities
that blockchain applications ofer to mitigate negative efects.
While other previous research focuses on the data-driven analysis on transaction records and token fow [20, 32, 39], but only few ofthem concentrate on the specifc topic of decentralized communities.
Friedhelm Victor et al. [53] presented a descriptive measurement
study to reveal the quantitative characters of the token networkby providing an overview of more than 64,000 ERC-20 token networks and pointed out that even though the token network on
Ethereum has followed a power law in its degree distribution, lotsof individual token networks are frequently dominated by spoke
pattern or a single hub. Similarly, Chen et al. [15] analyzed thewhole token ecosystem on Ethereum with emphasis on the token
creators and stakeholders. The result suggested that token holding
and token creation are profoundly unbalanced. These studies canbe based to some extent on our expectations about the distributionof governance tokens, which, as mentioned in Section 1, are thematerialization of the rights of community members. The imbalance in the distribution of power indicates a so-called decentralized
community is substantially controlled by some minority groups ofauthority, which further implies defciencies in the process of community empowerment. Furthermore, airdrop, the most dominantmeans of power allocation, has not been widely evaluated yet.
We consider our study a tentative attempt to derive the rationality of the airdrop model backward from the results regarding
users’ behavior after acquiring them. And more importantly, we
try to set up a research pipeline of role identifcation to understandand conclude the users’ composition and behavioral patterns in adecentralized community. We hope this work could provide referable information and data-driven evidence to support the further
design and research on decentralized governance models.

4

DATASET OVERVIEW

As we mentioned in Section 1, the Web3 community has very looseboundaries, i.e., as long as having a corresponding token, anyone
can be accepted as a member. For the sake of brevity and precision,in the rest of this paper, we will use Member to refer to all addresses

Making Sense of Roles in Web3 Community from Airdrop Perspective

holding PSP tokens in general and Initial Member to represent the
13,830 addresses that initially qualifed for and claimed the airdrop.

Figure 2: Procedure of Data Collection and Dataset Generate
In this paper, we collect 71,002 transaction records in the ParaSwap
community formed by 20,148 members, among which there are
13,830 initial members. The general process of our data collection isshown in Figure 2. Since the raw on-chain data can be understoodas bank transfer records, which are low in data dimension andcontain less valuable information, we spend extra efort on data
augmentation and pre-processing, such as compiling transaction’s
bytecode and interpreting the purpose of the function called. The
dataset is formed by the behaviors of three kinds of roles: smart contract, initial and later members, among which the initial members
are what interested us most. So, we collect the entire transaction
history of these addresses, and together with the PSP token records,
we generate a user profle and encode their behavioral patterns intoa cluster-solvable sequence form in the following section. Moreover, we set up two transaction graphs, PSP Transaction Graph andExternal Graph, aiming to unravel the occurrence of cliques andthe relationship between nodes.

4.1

Behaviors after Receiving Rewards

In order to explore the ParaSwap community member’s behavior
after the airdrop event, we have to categorize the transactions tosimplify the data. In Web3, it is granted that operations on tokens
can be traded on certain exchanges or direct transactions to an
address. However, there are two operations with more fnancial
properties that deserve more attention, namely Staking and Liquidity Providing (LP). Staking is a kind of fnancial service held byDApps, which ofers token holders a way of locking their assets
to earn passive income without selling them. As staking tokens
increases the number of locked positions, it reduces the liquidity inthe market, which in turn can contribute to the stability and evenappreciation of the coin price, which is positive for the members
holding them. LP is also a kind of passive income and increases thevalue locked to stabilize the price. The tokens fll the DEXs’ liquidity
pools to make it easier for other traders to exchange tokens. In summary, from the community’s perception, staking and LP are more
likely to be perceived as actions that beneft their interests and, toa certain extent, can be considered as a form of altruism since themembers performing these actions will sacrifce the liquidity of
their assets and opportunity for quick cash.
Table 1 illustrates the distribution of transactions made by the
initial members after the airdrop, based on three reward levels. It is

CHI ’23, April 23–28, 2023, Hamburg, Germany

noteworthy that the initial member who received 5,200 PSP tokens
made a substantial proportion of the Sending actions, however,the corresponding receiving transactions are comparatively low,
regardless of the reward category. This suggests that a signifcant
amount of PSP tokens were transferred to subsequent new members
through direct transfers. A similar trend can be observed in the
Selling and Buying behavior pair of trading.
Through simple calculation, we fnd that 103,745,200 PSP wereclaimed in total, from which subtract the remaining amount of
PSP in initial members’ addresses, we can get there are rounding
93,202,386 PSP fowed to the market, accounting for 89.34% of all.
Consequently, up to the moment of data collection on April 14�ℎ ,2022, there are 11,948 initial members, accounting for 86. 39% of all
who gave away all their tokens and thus left the community. Amongthese people who withdrew, 3,282, 4,885, and 1,843 belong to thecorresponding category of 5,200, 7,800, and 10,400. The attrition
rates for the three groups were 76.49%, 71.46%, and 48.20%.

4.2

Most Popular Services

We collect and organize a dictionary mapping 4,330 smart contract addresses with their names and functions to obtain a humanreadable interpretation for each transaction. To explore what kindsof smart contract services have gained the most interaction in thewake of the airdrop event, we list the top ten smart contracts thatreceived the highest number of transactions shown in Table 2. Not
surprisingly, the vast majority of the smart contracts on the list
belong to the type of fnancial services, as tokens from airdrop havenot gained universal purchasing capacity in a short period.

4.3

Network Construction

Based on the data obtained above, we collect the information about
ParaSwap members’ transaction records on Ethereum and create atoken network, PSP Transaction Graph, to perform temporal analysis, as shown in Figure 3. Specifcally, we construct a directed graphfor PSP tokens, where nodes stand for accounts and edges withweights representing transaction values. The network consists of
20,148 nodes and 46,410 edges for the PSP token transaction after
the airdrop event.
In addition, we are also interested in what happened before theairdrop event. Based on the full external transaction records wecollected, we generate an External Graph containing 221,864 nodes
and 1,111,209 edges for 7,003,317 external transaction history. Since
an external transaction usually means a simple p2p transaction, it is
not as informative as the PSP transaction graph, but it has a muchlarger and broader set of messages that can play an important rolein exploring the connection between addresses before the airdrop.

4.4

Temporal Evolution of Network

As shown in Figure 4, we compute selected properties by taking
the slice by the week, which means that we intercept the graphfrom the origin to the moment of data collection every other week,
including:• a. Reciprocity: Reciprocity originates from a social psychology concept that one positive behavior is rewarded by theperformance of another positive behavior, which is defned
as one of the defning attributes of any community [56]. The

CHI ’23, April 23–28, 2023, Hamburg, Germany

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

Table 1: Initial Member Behavior Statistics
Airdrop Type5,200 PSP
7,800 PSP
10,400 PSP

Quota
6,189
9,986
3,824

# Claimed
4,291
6,836
2,703

% Selling
36.40%
38.34%
35.33%% Buying
0.02%
0.09%
0.26%

Table 2: Top 10 Contracts
Name
Airdrop Contract
ParaSwap
Staking Pool 3
Uniswap V3: PSP
SushiSwap: PSP
Staking Pool 4
Uniswap V3: USDC-PSP
Staking Pool 1
Airswap
Staking Pool 7

Type
Airdrop
Trading: swap
Staking
Trading or LP
Trading: swap
Staking
Trading or LP
Staking
Trading
Staking% Staking
9.76%
13.66%
18.87%% Sending
31.76%
19.18%
11.95%% Receiving
0.07%
0.19%
0.11%

% LP
0.05%
0.10%
0.11%

will never leave the component once it enters the component,
while the term strongly connected refers to if every vertex
is reachable from every other vertex.

# Times
13830
3943
3400
2640
1868
1474
1452
697
631
512

Figure 4: Selected Parameters of PSP Token Transaction Network

Figure 3: PSP Token Transaction Network
friendliness of others leads to friendlier responses and more
willing to cooperate. Graph theory, refers to the likelihoodof nodes in a directed network to be mutually linked.
• b. Degree Assortativity Coefcient: Assortativity is employed to assess whether nodes that share similar degreevalues tend to interconnect [52]. A positive correlation in
network assortativity is observed when nodes with higher
degrees tend to associate with nodes that have a comparablylarge degree. If nodes with greater degrees tend to link to
nodes with a lower degree, the network is said to have anegative correlation.
• c. Number of Attracting Components: An attracting component in a directed graph G is a strongly connected component with the property that a random walker on the graph

From the reciprocity (Figure 4-a), it can be seen that at the outset,
there was only a one-way distribution of rewards to participantsfrom the airdrop address, but over time the reciprocity graduallyrises. As mentioned in the sociological explanation above, the wholenetwork of transactions and the participants’ connections in it arebecoming more stable and robust. Due to the presence of exchanges,
the degree assortativity coefcient (Figure 4-b) of this network
seems to be unsurprisingly negative, implying a high degree of centralization. However, this negative value tends to decrease slightly
over time, which can be explained by the fact that p2p transfers
are increasing. In other words, there is an increased tendency fora node to link to another node with the same level of degree as
itself. Again as the entire network began with airdrop contracts andmoved from being highly centralized to relatively decentralized,
the number of attracting components (Figure 4-c) also decreased
over time because as PSP token circulates, isolates and non-airdrop
addresses continue to join the market. The growth of the node andedge in the graph shows that they both gradually increase at the
beginning and stabilise over time (Figure 4-d).

5

METHOD FOR ROLE IDENTIFICATIONWith the advancements in artifcial intelligence, deep learning has
become a commonly used method to understand user behavior
by analyzing their interaction data [44]. However, this approach,despite being widely applied and requiring minimal domain knowledge, is not very interpretable. In particular, there is a lack of clarity
regarding the discovery of roles and the understanding of user behavior within the "black box" of deep learning. For blockchain data,Making Sense of Roles in Web3 Community from Airdrop Perspective

predictions can be made to identify attacks or money laundering[19], but there is currently no widely recognized training set toguide the behavioral patterns that emerge from the data-level features. As an alternative to deep learning, some studies have exploredthe use of unsupervised learning or principal component analysis toobtain interpretable clusters, which can be used to more efectively
capture and analyze the behavior of community members [1].

5.1

Labeling Member’s Behavior

For each address, we rearrange its activities into a transaction fow:
a discrete sequence of events, which describes the smart contracts
called by it, the number of PSP involved in the operations, andthe remaining PSP in its balance after the operation. We divide
the interactions between nodes into four categories: Trading, LP,
Staking and Transferring. Figure 5 illustrates the formatting of atransaction fow.

CHI ’23, April 23–28, 2023, Hamburg, GermanyÍ�� (� 1, � 2 ) = √Í� (� � 2 ) ×� =1 � 1,�

5.2

Feature Extraction

Our clustering algorithm is based on a similarity graph, where eachnode represents an address, and each edge represents a similarity
weight between two addresses’ transaction fows. We identify theaddress behavioral clusters by partitioning the similarity graph.
We extract transaction patterns from the fow as features tocompare similarities. Specifcally, we extract operation types ofthe ��ℎ address and reformulate an operation fow as a sequence�� = {� 1, � 2, . . . , �� }, where �� is the � �ℎ operation in this sequence,and � is the total number of items in the sequence. We use S todenote the set of all sequences and � to represent the total numberof sequences in S. However, the diference in the length of operationfows make it difcult to measure the similarity. To solve the above
problem, we denote eight kinds of operations with � as the index.
After calculating and organizing the number of operation types
in every address, we can obtain the features of the ��ℎ address as�� = {� 1, � 2, . . . , � 8 } , where � � equals to 1 or 0. When it is equal
to 1, the address has done this operation. When it equals 0, thisoperation has not been performed by the address. After extracting
the features of addresses’ behaviors, we choose weighted cosine
similarity over other alternatives (e.g., Euclidean distance) because
it is more suitable to handle the highly sparse matrix [27].

√Í� (� � 2 )� =1 � 2,�(1)

� (� 1, � 2 ) ranges from 0 to 1, and a small distance value indicatesa high similarity between two transaction fows. � � refers to theweight of the � �ℎ feature, whose value chosen in our method isfundamental to the cluster results. � is the number of elements in�� , which is 8 in this case.

5.3

Clustering Algorithm

We employed an agglomerative hierarchical clustering (AHC), which
has less time complexity and better computational stability [59].
Specifcally, AHC procedures start with � singleton clusters andrepeatedly merge clusters until a specifc level of granularity has
been achieved, i.e., the number of clusters reaches the target. The
AHC has three main distance measures: single linkage, complete
linkage, and average linkage [59]. In most cases, the AHC withsingle link is relatively stable and efective for linear, manifold, andconvex data. Another important issue is how to obtain the suitablepartition from several clustering results, which can be characterized
as the problem of determining the optimal number of clusters �.

5.4

Figure 5: The Process of Formatting Transaction Flow

� =1 (� � � 1,� ) × (� � � 2,� )Determining the Number of Roles

In our approach, the number of clusters, �, is a free variable, andhow to decide its value will be the key to the whole process. Toohigh or too low a value of � will have a signifcant impact on
the results, especially in terms of interpretability. To overcome theproblem, we used the Silhouette Coefcient [3] to select the optimal
� in AHC . Specifcally, we trained the data on AHC with � ranging
from 2 to 20 to determine the optimal number of clusters. We fnd
that the AHC with � ∈ [12, 20] seems to be a good option in thiscircumstance.
After calculating the transaction distribution for each cluster
and comparing its diferentiation from other � value results, we
fnd that the most suitable solution is when � = 14, which results ina relatively balanced number of addresses among the clusters, but
at the same time maintains independence between, without fusing
important and distinct characteristics.

5.5

Cluster Results

After determining the parameter, we can proceed with the model to
get the initial members’ roles in the airdrop community. The result
generated 14 clusters, and the t-Distributed Stochastic Neighbor
Embedding (t-SNE) visualization is shown in Figure 6.
In Table 3, we label the clusters with an index and description of
their proportion, source of income, and ways of consumption. It is
worth noting that the algorithm captures the type of operation of
the members, and the ways of consumption describe precisely their
behaviors belonging to the corresponding cluster with very clear
boundaries. This means that 75.97% of the members performed asingle type of operation after the airdrop event. Data from transaction fow shows that most of these operations were done once,
which is consistent with the context of high Ethereum commission
fees. As part of the single-operation clusters, 61.03%, or clusters 1
and 2, exited the community in a short period after obtaining an

CHI ’23, April 23–28, 2023, Hamburg, Germany

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

Figure 7: Proportion of Airdrop Tiers in Clusters
activity enhances the stability of the issued tokens. However, thissegment accounts for less than 6% of the total addresses eligible
for the airdrop. How to increase the percentage of these addresses
should be of interest and research to project owners.

5.7
Figure 6: t-SNE Processed Visualization of Cluster Results
airdrop, which is also validated and in accordance with the member
attrition scenario we discussed in Section 4.1.
Table 3: 14 Clusters of Members in ParaSwap Community
Index
1
2
3
4
5
6
7
8
9
10
11
12
13
14

5.6

SourceAirdrop(Group 1)

Airdrop + Buy(Group 2)

Consumption
Selling
Sending
Staking
Staking+Selling
Holding
Selling+Sending
Staking+Sending
LP+Staking
LP+SellingLP
LP+Sending
Staking+Sending/Selling
Sending/Selling
LP+Sending/Selling

Percentage
38.79%
22.24%
14.94%
8.50%
4.87%
2.39%
1.71%
0.39%
0.30%
0.11%
0.09%
3.58%
1.91%
0.18%

Composition of Airdrop Tiers in Clusters

By calculating the proportion of reward recipients at diferent tierlevels in each cluster, we obtain Figure 7. On the whole, addresses
belonging to the 7,800 tier occupy a signifcant majority in eachcluster, which is inevitable, as the number of claimants for thethree-tier rewards of 5,200, 7,800, and 10,400 are, respectively, aratio close to 3 : 5 : 2.
Taking the above factors into consideration, we can say thatmembers with proactive token purchasing behavior are statistically more likely to be involved in more permanent and substantial
fnancial activity. Initial members with these characteristics arewhat DApps want to see, as their active participation in staking

Implications for Buying Behavior

We defne the operation of exchanging PSP from a DEX as a buying
behavior, which means that these members in addition to airdrop
rewards, in making the act of collecting tokens with subjective
motivation. So, naturally, the research questions such as "What are
the qualities of an address that has a buying behavior?", "How does
this group of buyers difer from other groups?" arise.
Shown in Table 3, we mark the cluster index from 1 to 11 asGroup 1, whose only source of PSP token is airdrop, and the index
from 12 to 14 as Group 2, whose performed buying behaviors. We
selected three indicators: Balance, Staking, and Liquidity to discuss
the distinction between the two groups in terms of duration period,and quantity held. We defne the sum of the number of days thata fnancial activity is carried out by an address until the complete
withdrawal of capital as Period, while the weighted average ofthe number of tokens corresponding to the duration is defned as
Quantity, which in this case is of course number of PSP holding.
In Figure 8 a, we ft their period distributions with probability
density function (PDF) in order to reduce the gap between Group 1
and Group 2 in terms of the amount of data. The overall perspectiveof the total density distribution shows that Group 2 has a wider
distribution than Group 1. In the leftmost Balance Period, there is arelatively clear concentration at 0 and 147 on the x-axis, meaningthat initial members are more likely to remove their PSP tokens
on the day they receive a short or to continuously hold it until the
day we made a slice to collect the data. It is worth noting that in
the rightmost liquidity period distribution, the maximum duration
value is just under 40, which means that for both groups, overall,
all members are not very willing to provide liquidity.
In Figure 8 b, we plot the density functions for the quantity oftokens of Group 1 and Group 2. There is a clear diference between
the three subgraphs here and those above: the top graph PDF curveis smoother, while the bottom one is more serrated, which indicatesthat the Figure 8 b has a more discrete distribution of values, caused
precisely by the fxed amount of three airdrop slots. Starting withthe distribution of balances on the left, there is no doubt that Group2, which has "purchases" as an additional source of PSP tokens, hasmore members distributed over a higher quantity of tokens. As forthe middle graph of staking and right graph of liquidity, Group 2Making Sense of Roles in Web3 Community from Airdrop Perspective

CHI ’23, April 23–28, 2023, Hamburg, Germany

Figure 8: Fitted Period Distribution and PSP Token Distribution with PDF
also outperformed Group 1 in both respects, with the majority ofmembers in Group 1 having no staking or LP action at all, which
results in missing data. The only samples stay low and concentrated
around the origin.

5.8

Discussion on Role Identifcation

Fourteen clusters are, as yet, way too many for conducting higherlevel analysis and discussion. Coupled with the fact that we noticed
that clusters are logically connected with each other in actions
beyond the data level, we re-integrated the clusters into fve roles.
We next elaborate on the rationale for the reintegration and will
discuss the instructive signifcance of the roles and classifcation
methods in the next section.
5.8.1 Speculators (41.18%): Cluster 1, 6. Speculators will choose to
sell the airdrops through CEXs or DEXs without any other operations after obtaining the airdrops rewards. The motivation behind
their almost immediate withdrawal from the community was most
likely purely proft-driven. Speculators may have given a returnthat was just able to cover their costs when they participated in
the preliminary activities. Based on their judgment, selling PSP in
the short term would yield the greatest return, which also implies
that they hold a pessimistic attitude about the future developmentof the agreement. Otherwise, promising development would bringthem higher expected returns.
So, can we consider the presence of speculators, especially as initial members, to impede the development of the Web3 community?
We suggest that the answer is no. There are plenty of speculators
in whatever market, and even more so in the context of blockchain,

known for its free trade. This should be treated as an immutable environment precondition. Speculators are bringing in new members
as they exit the community. But DApps need to be wary that once
the number of speculators becomes too large and the second andthird-generation members all hold this pattern of behavior, it willlead to the ponziization of the protocol and bring about a difcult
reversal of the predicament [7].
5.8.2 Diamond Holders (20.31%): Cluster 3, 5, 8, 10. Diamond Holders are those initial members who hold PSP until April 13�ℎ 2022
without selling or sending PSP. They are considered optimistic
about the long-term development of the community in terms of theappreciation of token price, hoping to achieve greater income inthe later stage. We further divide two sub-categories in Diamond
Holders:• Risk-averse Holders (4.87%): They are risk averse, refusing
to participate in the risky activities associated with tokens,such as staking and LP, despite the potential income.
• Risk-seeking Holders (15.44%): Diferent from risk-averse
holders, risk-seeking holders are keen to participate in risky
activates.
Diamond Holders are undoubtedly one of the most welcoming
categories of community members. They are willing to give up
quick cash and bear the opportunity cost11 of holding positions andcontributing their assets to the PSP price. In contrast to speculators,
they have confdence and are qualifed to participate in the future
voting of the community for holding governance tokens.
11 The concept of opportunity cost is fundamental to the economist’s view of costs.

Since resources are scarce relative to needs, the use of resources in one way prevents
their use in other ways.

CHI ’23, April 23–28, 2023, Hamburg, Germany

5.8.3 Airdrop Hunters (22.24%): Cluster 2. We suggest that the vast
majority of the initial members belonging to cluster 2 should be
afliated accounts controlled by a single individual, since the riskof p2p trading with strangers is often unacceptable without smart
contracts as intermediaries rather than direct transaction. We will
prove this hypothesis later in Section 6.2.
5.8.4 Diversified Members (10.62%): Cluster 4, 7, 9, 11. Diferent
from speculators and airdrop hunters, Diversifed Members choose
to take part in multiple activities. They are involved in staking
and LP, but at the same time, they do send and sell. Members of
this composite behavior pattern are more likely to be independent
individuals, as savvy Airdrop Hunters tend to aggregate PSP in one
place before staking or LP to reduce multiple commission fees.
On the one hand, they pursue more returns by participating instaking and LP. On the other hand, they also sell PSP for a proft
when PSP reaches a proper price. Their decision patterns are relatively more dynamic and fexible. In a sophisticated decentralized
community, this group of members should make up the majority, but we only fnd around ten percent of them in the ParaSwap
community.

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

on anonymous and highly market-oriented blockchain platforms
than in real communities [10, 23].

6

5.8.5 Buyers (5.67%): Cluster 12, 13, 14. Also welcomed by the community, but unlike Diamond Holders, Buyers are more proactive inpurchasing PSP. As we mentioned in Section 5.7 for positive implications of purchasing behavior, we classify this group independently
as a role.
5.8.6 Summary. To put a summary at the end of this section, we
would like to discuss the potential contribution of role identifcation
to decentralized community governance and airdrop distribution
methods as a practical guide, which can also be used as a universal
methodology to understand the human engagement within otherblockchain based community.
We found that the diferential airdrop can infuence the behavior of initial members to a degree. Members who receive more
rewards have more spare capacity to invest in positive actions forthe community. We cannot conduct a profound analysis on thecost of acquiring airdrop per initial member in this paper because
it involves extremely complex calculations and system design. If
rewards can fll the cost of early-stage participation, members will
have fewer concerns about staking and LP. A more accurate prediction when setting the diferential regulations, designed to ensurethat members do not lose money without afecting the stability ofthe token model, would provide a stronger incentive.
On a more assertive side, the community could split the planned
airdrop into multiple installments or set additional rewards. Specifically, the protocol can perform role identifcation with respect
to the behavior of the address after the frst few airdrops, during
which they investigate the role dynamics of members, including
how their role changes over the cycles and what kind of factors canpreserve long-term participation in the community. The project
owner can decide on the airdrop list through the above process
and result. While such a policy is somewhat paternalistic, and wewould like to see more spontaneous enthusiasts, on a result level,
the tempting additional rewards could lead to more positive behavior. Community rule makers also should not have the illusion of
being overly idealistic; a proft-oriented mindset is more prevalent

NETWORK ANALYSIS

We try to explore the behavior of the initial members through component analysis on PSP Transaction Graph. The existence of CEXs
will cause great confusion in the transaction analysis, making fnding components extremely difcult. The exclusion of CEXs may
cut down on the number of components, but could provide a morecredible dataset. Hence, we focus on those components that consist
entirely of direct transfers in order to obtain more accurate andcredible results. Through the process of cutting of contract addresses and isolates, we obtain 2,493 components consisting purelyof 7,982 wallet addresses and label them with corresponding IDs.

Figure 9: Typical Structure of Chain Transaction

6.1

Typical Forms of Component

We found a large number of records of p2p transactions in Section
5.4, so how these PSP were transferred became a natural question.
For 2,493 components with marked IDs, we recorded the number ofnodes and edges as well as the edges’ weight, the number of initialand newly-generated members, and reciprocity characteristics toproduce a profling dataset. On average, each component consistsof 3.20 nodes, and there are 586 components containing more than
three. 3,219 out of 7,982 second-generation members are involvedin these p2p transaction components. On average, each component
contains at least 1.38 second-generation members, and only 35 ofthem do not contain any. When we plotted all the components containing more than three nodes, we noticed some special structuresthat frequently appeared and immediately associated them withpotential arbitrage behavior. We named the two typical structures
as Chain and Sunfower according to their propagation method. We
use diferent node colors of Orange to represent initial members
and Sky Blue to represent later members. In order to have a better
visual perception of a white background, we use the cool colormap
array12 , which changes its color from blue to purple as the edge
weight increases.
As shown in Figure 9, we fnd two special paths of one-way token
transfer among components, which we call the "Chain" transaction.
The pattern starts from one or multiple initial members and continuously propagates the PSP it received to its successors. As shown inthe subgraph on the left of Figure 9, in the component ID-831, eight
nodes of initial members pass their tokens to a second-generation
member. The edge color between the nodes is from shallow to deep,
12 https://www.mathworks.com/help/matlab/ref/cool.html

Making Sense of Roles in Web3 Community from Airdrop Perspective

representing the increasing number of PSP passed like a Fibonacciseries. The other one-way pass structure of ID-3 has more of amerging and splitting pattern, as shown on the right.

CHI ’23, April 23–28, 2023, Hamburg, Germany

giving the community a way to track down potential hunters or
Sybil attackers and eliminate the risk before they cause signifcant
damage.

Figure 10: Typical Structure of Sunfower Transaction
Another transmitting pattern we called "Sunfower" is shown in
Figure 10. The PSP obtained from the initial members is transferred
to an eligible address, which performs a single operation on thePSP. The diference between these two approaches is that the initialcentral member in the right-hand side of Figure 10 acts as a transit
vertex to transfer the collected PSP to another newly-generated
member.

Figure 11: Alternative Structure of Sunfower Transaction
The two patterns shown in Figure 11 also aggregate PSP to a single node. But unlike the "Sunfower" transaction, the central nodes
are non-airdrop nodes. We dig into the transaction history of thesecentral nodes and notice that they have no record of interaction
with any smart contract but only serve as a staging area, which prevents the airdrop nodes from directly interacting with each otherin order to ensure the independence between them. If without a
network-wide screening of transactions, it would be difcult for
the DApps to exclude such components.
Admittedly, discovering these components based on the PSP
token network while proving the existence of potential arbitrage
is a kind of hindsight. DApps would undoubtedly like to have discovered these possible arbitrage behaviors before the airdrop event.
However, this does not diminish the contribution of this section.
First of all, the emergence of this sort of aggregation patterns has
demonstrated the vulnerability of the ParaSwap community’s mechanism for fltering hunters. A component with a more signifcant
number of members, such as ID-100, is capable of holding 130,000
PSP in a single individual and has more than 20 times the voting
power of the average member. In addition, we demonstrate thatcomponent analysis remains essential even after the airdrop event,Figure 12: Clique ID-309

6.2

Airdrop Hunter Cliques

By comparing the external graph before the airdrop event with the
PSP transaction graph after it, we attempt to fnd the hunters thatare not allowed by the ParaSwap policy, where one individual ororganization controls multiple addresses to obtain multiple rewards.
As mentioned in the above section, the presence of CEXs would
substantially obfuscate the fow of tracking transactions, we choose
to deal with the most intuitive and obvious p2p token transactions.
We numerically assign each component with ID and exclude behaviors that have a low probability of being identifed as arbitrage,such as those components consisting of only two or three members,
and instead focus on subgraphs with more than fve nodes, whichis the threshold of getting ParaSwap’s focus when examining eligibility13 . Our goal is to evaluate existing allocation methods todiscuss their plausibility and discover what methods hunters use
to escape scrutiny from DApps. To address this issue, we retrieve
the members’ information of components appearing in the PSP
transaction graph and match them to the external graph to discover
their interrelationship before detection.
6.2.1 The Organized and Planned Hunter. This small clique’s center consists of 4 non-airdrop addresses, as presented in Figure 12.
The other 17 initial members were not related in any way prior
to the award, but they were all funded by these four "sponsors"
through direct transfers to provide funds for airdrop eligibility. This
sponsorship was sophisticated planning, with each initial member
being backed up by multiple sponsors, and the PSP tokens weresmoothly returned by the sponsorship route as designed after the
rewards were distributed.
Given that registering addresses on the blockchain costs nothing,and since there is no direct connection between addresses participating in airdrop slots, community regulators can barely identify
and decipher fake airdrop bidders’ behavior generated by "Sponsorship Models" like this, unless they look further than their second
degree of association. However, given social and public opinion
considerations, a strict but imprecise screening policy could trigger
13 https://medium.com/paraswap/whats-an-active-user-clarifying-psp-token-

distribution-fltering-logic-81df6096d410

CHI ’23, April 23–28, 2023, Hamburg, Germany

more intense resistance from already disgruntled users, exposing
the program to irreversible reputation damage and user loss.

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

available, we do not know what happened. But from a researcher’s
perspective, there is no doubt that arbitrage undermines the degree of decentralization that is the very core concept of blockchain
platforms and Web3 communities.

6.3

Figure 13: Clique ID-381
6.2.2 The Cautious Hunter. This small group is a typical aggregation pattern with more than fve participants judging from the
PSP transaction graph on the right in Figure 13. Only seventeen of
the nineteen initial members were present in the external graphbefore 15�ℎ November, but they are not fully connected by edges.
Based on the strong association of these addresses embodied in the
PSP transaction graph on the right, it is reasonable to believe that
other discrete nodes in the external graph are likely to be fundedthrough CEXs, which means they can be funded without traces inour external transaction history or some other sponsoring nodethat strays from the airdrop reward list.

Figure 14: Clique ID-776
6.2.3 The Blatant Hunter. Figure 14 exhibits part of the typical
pattern of a large number of prevalent components that are heavily
linked in the external graph and aggregate the resulting rewards
to one initial member after the airdrop is issued. They are often
composed of exactly fve nodes, which is right under the detection
conditions claimed by ParaSwap.
However, how should we think about this category of hunters
who are on the edge of the rules? The contributions they maketo qualify for airdrop meet the criteria of the community rules
in monetary terms, but does this mean that the relationship between hunters and the community can be eased? Although, at thispoint, ParaSwap has not made its own screening code publicly

Discussion on Hunters and Network
Analysis

While there is much research now seeking to depict a vision of aharmonious, equal, accessible, and open Metaverse or Web3 community [16, 54], we have to be reminded that we are dealing witha feld that contains complex behavioral studies under anonymity,
sophisticated economic models, and ferce mistrust as a foundation.
Like the real-world problem of resource allocation and how government grants are distributed to stimulate economic recovery, thisproblem in the blockchain community may be a microcosm of themacroeconomic problem. Airdrop intends to increase user retention, distribute governance tokens to members who are genuinely
engaged in the community, and decentralize DApps for stable longterm growth. However, the p2p cliques that we detected included
more than half of the initial members, and the real situation isundoubtedly more than that, considering the prevalence of CEXs
and mixing services [58]. The number of organizations or individuals operating behind these addresses will be far smaller than thenumber of users shown on the surface. With the presence of sucha large percentage of addresses suspected of hunting airdrop, thestability of the airdrop currency issued would be detrimental oreven disastrous.
The most immediate damage is monetary. As with stocks, asell-of will bring down the price, but unlike stocks, free token
trading on the blockchain does not have a mandatory circuit breakermechanism. All holders of tokens will collectively face a price drop,at which point an avalanche-like chain reaction will send holders
scrambling to sell in time to stop losses that can be enough to wipeout a token’s value.
Systematically, the decentralization of DApps has also been undermined. It is worth noting that PSP Token also carries with it the
responsibility of governance, meaning that in receiving rewards
from airdrop, addresses simultaneously gain their respective degrees of voice and voting power, and holders are entitled to voteon decisions for the community. However, the presence of hunters
occupies the rights that should be given to dozens of people into
the hands of a small number of actual address holders, which isenough to cause the divergence or even the demise of a decentralized community, as the example mentioned of the Juno Network.
When the decentralization of a DApp is null and void, and from themonetary aspect, it becomes unproftable for its users, it is hard
to imagine any other basis for its continuation. On a higher level,
the instability caused by the challenges of decentralization will
inevitably damage the credibility and growth potential of a DApp.
We believe that improvements can be made in terms of bothscreening methods and distribution policies. Although there is anearly impenetrable and untraceable black box of CEXs, project
owners should be able to track the network of more than seconddegree relationships based on token transfers, thus excluding huntercliques such as the "sponsor model" we fnd above. Or by comparing the behavior of addresses before and after the airdrop, using

Making Sense of Roles in Web3 Community from Airdrop Perspective

whether or not they are qualifed as a label for the training set
to obtain a classifcation algorithm for external graph features toflter at scale. In terms of the airdrop policy, we fnd that a tiered
system of rewards will result in addresses with high rewards being
more likely to engage in subsequent benefcial behaviors. More detailed and sophisticated tiered models may lead more participants
to engage in positive activities like staking or providing liquidity.
Apart from the cold and emotionless code design and sophisticated model, we believe there is a more HCI way to the solution ofbringing altruism into the Web3 community. We need to investigate
how to reduce the generation of things like arbitrage, aggressive
behavior, and inequality at the motivational level in a community
with a high degree of anonymity. As pointed out by Elsden et al.
[18], the most central issue appears to be some fundamental human challenges, including fnancialization [43], procedural trust,algorithmic governance, and front-end interactions. We suggestthat in terms of Web3 community governance, the improvement
can be conducted in two ways. The frst is at the engineering level,
where we argue that a fgurative community enhances the senseof belonging of its members to a greater extent, which in return
brings in more altruists. For example, a community of decentralized games can build stronger ties between players and content,
and between players and players, than in a fnancial type DApp.
Currently, no Metaverse-level platform can dominate and provide aspace for digital people to exist in the true sense of the word. Thismeans that a member in a loose community is nothing more thanan unreachable piece of hexadecimal address to other members.
Second, in terms of policy formulation, we argue that community
leaders should always be wary of the negative efects of fnancialization. The blockchain platform is a huge and free market, and theimpression it brings to the outside world has always been portrayedby the media as a shortcut to overnight riches, which in part has
led to the current speculative culture in the entire feld.

7

DISCUSSION AND FUTURE VISIONOur research shows that, at this stage, decentralized communities are far from what they were designed to be. Airdrop huntersand speculators who might crowd out the space of community
enthusiasts force other willing community members to give up
their community-benefcial behavior. We conclude our result as
challenges for future research with conceptual and methodologicalsolutions to the current predicament.

7.1

Expand the Ways of Interaction

Studies on the infuence of technologies and user experience (UX)
design have suggested that a refned interaction can cause positive
efects on user participation [48, 49]. Of-the-shelf measurement
frameworks have also long been available [9], but few of us bring theblockchain interaction down to the practical experiment. Although
the blockchain network is claimed as Web3, from a user experience
perspective, blockchain games, fnancial services, and other DApps
are still in their primitive stage after so many years compared tothe traditional Internet. Many studies have focused on designinguser interactions for real-life Internet of Things applications rather
than for virtual social network interactions [21].

CHI ’23, April 23–28, 2023, Hamburg, Germany

We propose that in the future, with the rise of technologies suchas VR/AR [22, 45], a pervasive and ambitious blockchain social
network, such as the Metaverse, could be established to trigger
users’ motivation for engaging the community. However, it does
not necessarily require cutting-edge technologies like VR/AR, a
set of social layers of virtual identities on top of real ones while
protecting anonymity can be applied and tested for its positive
infuence. For communities running on the particular infrastructureof blockchain, we currently know very little about the impact ofinteraction on these blockchain users and do not know if traditional
approaches can motivate them or build strong connections or asense of belonging between members and the community.

7.2

Slow Down the Pace of Financialization

As far as the decentralized community level is concerned, the ideaof slowing down the pace of fnancialization is to separate the governance from tradable tokens. The fnancial attributes of blockchaincause a zero-sum game scenario that can signifcantly reduce thelikelihood of cooperation among people. Giving the token both fnancial and governance features could be dangerous, as if allowing
vote trading in real-life politics. The factual data results, as discussed in Section 4, show 86. 39% of initial members sold out their
PSP and thus left the community within six months. This phenomenon indicates that most of the initial members are attracted to thefnancial attributes of airdrop than the governance attributes. Considering the more than two million airdrop screening participantsin the case of ParaSwap, fnancialization is an excellent incentive toattract users in the short term, however, in the long run, it cannot
help retaining users and promote community engagement.
How do we design and evaluate a separate governance system?
We consider that a new ERC token standard could be proposed tocreate a non-tradable, or worthless, voting ticket for participationin community governance, solving the problems in a blockchain
way. However, separating the two would violate the design essenceof property and ownership of digital assets. Another incentive
mechanism can be applied in the form of rewards engaging thevoting layer. Although what discussed above is just a brief idea, thekey point is that the HCI community could play an important rolein prototyping, envisioning, and evaluating such models.

7.3

Improve the Model of Community
Governance and Empowerment

Ethereum ofers any individual or organization a chance to establish
their own circulable tokens with very relaxed restrictions on setting
regulations and distribution models. It is the equivalent of a New
York Stock Exchange with no vetting or restrictions, where anyone
can conduct an initial public ofering (IPO) to raise capital. Smalland medium-sized teams with potential and strength have more
opportunities to express their ambitions, but the majority left are
frauds, bubbles, and Ponzi schemes [25, 51].
This hostile environment is one of the primary reasons preventing the development of stable decentralized communities. The
eforts of researchers in this area should be instrumental in developing rules and policies, and the design of resource allocation by further understanding blockchain users and their roles. To give some
simple, unexplored examples, we have difculty understanding

CHI ’23, April 23–28, 2023, Hamburg, Germany

the behavior patterns of community builders and project initiators.
Are there certain characteristics of their outreach on social media,
such as Twitter? Do their conversations in discussion groups refectpotential fraud? What kind of white paper attracts investors the
most? How does a committee of decentralized community work?
Essentially, we try to show that exploring HCI in the blockchain is
not limited to the user side but should also look broadly at otherparties, such as rule makers and companies holding huge amountsof money, and design policy and resource allocation models thatcan coordinate the relationships between these parties.
Moreover, as outlined in the conclusion of Section 5.8, we proposed a multi-staged airdrop model that releases the second roundreward based on the role identifcation of the initial round. Similarly, there is still signifcant potential in the airdrop strategy andthe process of empowering participants, which are vital elements
in constructing decentralized communities.

7.4

Understanding the Perception of Members
in Decentralized Community

Understanding users’ behavior and values is crucial for incentivizing and building a community. If we can summarize their behavioral
characteristics as well as the values and ideologies [33] driving behind them, perhaps the answer can be more easily obtained. On a
cognitive level, the blockchain is still in its infancy and requires
user education and a new way of thinking [2]. We lack evidenceof the linkage between the user’s level of professional knowledge
and willingness to be a contributor. Will a sophisticated member bemore proft-oriented? Or will a newbie dedicate more to the community? Although some blockchain-based social or instant messaging
platforms have started to emerge in recent years, it is extremely
difcult and time-consuming to investigate members of decentralized communities, especially those who are considered speculators
and airdrop hunters behind the heavy curtain of anonymity.
As an example of our work, we could obtain more valuable information if a qualitative study on roles is conducted, but reaching asufcient number of ParaSwap users to produce valid results is diffcult given the background of the anonymous nature of blockchain.
For role identifcation, although we have successfully labeled andcategorized community members in terms of objective results, perhaps the categorization method would be more convincing if we
recruited several users for each type of role for interviews and combined their motivations and behaviors for discussion. For cliquedetection, there is a high probability that airdrop hunters capableof conducting Sybil attack-like operations are people with considerable experience and background knowledge of blockchain technology, which could indicate that a profound understanding may notlead to an altruistic community member. However, this hypothesis
can only remain at the conjecture stage until there is defnitive
evidence of the investigation. To use a more precise example, wewant to know what factors lead to positive or negative behavior.
For Diamond Holder, we want to understand their reasons for holding PSP for a long time and the motivating factors behind thesereasons, such as the amount of digital assets they hold, their viewson the future development of the project, etc. For Buyer, we cansummarize their commonalities to deduce the reasons behind theirbuying behavior. In addition, when we fnd that interviewees have

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

multiple addresses classifed as diferent roles, qualitative study
results can indicate users’ habit of using multiple addresses.
In addition to surveys of certain specifc users, a large-scale study
on an open social platform or discussion group analysis would also
be helpful. Jahani et al. [28] conducted an exploratory study of thecharacteristics of online discussion around cryptocurrency forums.
They concluded that projects with more information available andhigher levels of technical innovation are associated with higher
discussion quality through regression analysis. This kind of social
computing research, which is not a stranger to us, would provide a
wealth of topics worth exploring if it could be linked to the results
generated from transaction records. For example, in a decentralized community with a high percentage of speculators and airdrop
hunters, what kind of characteristics does the discourse on theforum have? Or conversely, we can explore whether a harmonious
communication atmosphere is more likely to lead to an altruistic
community environment.

7.5

Future Challenge for Decentralized
Community in Web3

As aforementioned in Section 3.2, unlike studies on community governance and moderation on Web 2.0 [14, 31], most of the current
blockchain community is more concerned with the development direction and resource allocation policy of DApps. However, with therapid development of blockchain, some content-centric supporting
infrastructures and corresponding streaming or social DApps suchas Odysee14 are being developed [36]. In general, decentralized,
online social media built with distributed storage as the back-end
is not a new concept. They try to build a social network with nocensorship, no single point of failure, and user autonomy that can
support content verifcation through trust, and reputation [34].
Blockchain, in this situation, plays a fundamental role by providing a transparent, immutable underlying facility for information
storage and retrieval [42], proof as a service [18], and incentives
for providing content or authentication [6].
However, when DApps begin to evolve from such token exchange services into things that can be used by the average Internet
user on a daily basis, away from the abstract virtual fnance andcyberspace, how do these ahead-of-the-curve and idealistic products face real-world regulations? For example, Sağlam et al. [8]pointed out that only 27.5% of blockchain systems covered European Union’s General Data Production Regulation (GDPR) throughlegal documents with privacy policies or terms and conditions. In
addition to the internal aspect of privacy protection, the external
threats of violating behavior, such as infammatory speech, plagiarism, harassment, or even scams and crimes of cryptocurrency, still
exist. Blockchain social network systems may introduce more newproblems while keeping all the issues of the Web 2.0 community,
which seems difcult to address, rely on loose community members without a robust executive body, or compromise the degree ofdecentralization. We propose that this specifc moderation issue ofusers as shareholders on an equal footing with developers needsdiferent ways of design and evaluation from the "Manage - Regulated" model of Web 2.0. But on the other hand, blockchain, as a new
14 https://odysee.com/

Making Sense of Roles in Web3 Community from Airdrop Perspective

infrastructure, may technically give some existing Web 2.0 community moderation tools new possibilities and scope to perform.
For example, for the governance of online speech [46], the easy
incentives facilitated by blockchain can support the establishmentof positive role models or the tracing and discourage anti-social
behavior with its public and transparent records. Mechanisms likegovernance tokens as rewards could also be used to select members from the community as stewards, giving them more power tofurther drive the community in a favorable direction.
But the challenge that we think is more worthy of attention issomething more fundamental: the process of building community.
We see our work not as being limited to a specifc decentralized
community or scenario, but as an exploration of the airdrop, as partof building the community and empowering its members. As theprevious step to operating and governing a community, the growing stage of a decentralized community should be spent with thesame research efort. While the vast majority of addresses exhibit
arbitrage behavior, Web3 builders may need to rethink whether theusers share their values and are ready for decentralization.

8

CONCLUSION

To sum up, in this paper, we use a detailed data-driven approachto understand altruistic and proft-oriented members in a decentralized community. We explore the rationality of airdrop as a kindof incentive mechanism and afrm the contribution of part of thisallocation method: the diferential tiers can be a potential method
to encourage community-benefcial behaviors. We also perform a
network analysis to identify the airdrop hunter’s methods throughmultiple accounts. Our fndings lead us to a discussion on the underlying causes of the current predicament and future direction of
the decentralized community in Web3. We suggest that taxonomy
of roles based on the public data could be a promising solution to
support the empowerment process of the community. We propose
that, beyond the technical difculties, between the current decentralized community and the goals of the ideal Metaverse, there arestill complex issues related to human factors, social organization,and fnancialized markets that require the participation of the HCI
community. Moreover, with the development of Web3, we also raisepotential governance issues regarding blockchain-based content
platforms or social networks by analogy to Web 2.0. We hope that
our work could play a role in advancing the understanding of thisunique emerging technology and form of community, and further
motivate researchers to push forwards the boundaries and expand
the possibility of governance.

ACKNOWLEDGMENTS
This work was supported by Shenzhen Science and Technology
Program (Grant No. JCYJ20210324124205016).

REFERENCES[1] Hervé Abdi and Lynne J Williams. 2010. Principal component analysis. Wileyinterdisciplinary reviews: computational statistics 2, 4 (2010), 433–459.
[2] Abdulla Alshamsi and Prof. Peter Andras. 2019. User perception of Bitcoin
usability and security across novice users. International Journal of HumanComputer Studies 126 (2019), 94–110. https://doi.org/10.1016/j.ijhcs.2019.02.004[3] S. Aranganayagi and K. Thangavel. 2007. Clustering Categorical Data Using
Silhouette Coefcient as a Relocating Measure. In International Conference on
Computational Intelligence and Multimedia Applications (ICCIMA 2007), Vol. 2.
IEEE, Sivakasi, Tamil Nadu, 13–17. https://doi.org/10.1109/ICCIMA.2007.328

CHI ’23, April 23–28, 2023, Hamburg, Germany[4] Javier Arroyo, David Davó, Elena Martínez-Vicente, Youssef Faqir-Rhazoui, andSamer Hassan. 2022. DAO-Analyzer: Exploring Activity and Participation in
Blockchain Organizations. In Companion Publication of the 2022 Conference on
Computer Supported Cooperative Work and Social Computing (Virtual Event, Taiwan) (CSCW’22 Companion). Association for Computing Machinery, New York,NY, USA, 193–196. https://doi.org/10.1145/3500868.3559707[5] Marcella Atzori. 2017. Blockchain technology and decentralized governance:
Is the state still necessary? Journal of Governance and Regulation 6 (03 2017).
https://doi.org/10.22495/jgr_v6_i1_p5[6] Cheick Tidiane Ba, Matteo Zignani, and Sabrina Gaito. 2021. Social and Rewarding Microscopical Dynamics in Blockchain-Based Online Social Networks. In
Proceedings of the Conference on Information Technology for Social Good (Roma,
Italy) (GoodIT ’21). Association for Computing Machinery, New York, NY, USA,
127–132. https://doi.org/10.1145/3462203.3475913[7] Massimo Bartoletti, Salvatore Carta, Tiziana Cimoli, and Roberto Saia. 2020.
Dissecting Ponzi schemes on Ethereum: identifcation, analysis, and impact.
Future Generation Computer Systems 102 (2020), 259–277.
[8] Rahime Belen Sağlam, Çağri Burak Aslan, Shujun Li, Lisa Dickson, and Ganna
Pogrebna. 2020. A Data-Driven Analysis of Blockchain Systems’ Public Online
Communications on GDPR. In 2020 IEEE International Conference on Decentralized
Applications and Infrastructures (DAPPS). IEEE, Virtual Event, United Kingdom,
22–31. https://doi.org/10.1109/DAPPS49028.2020.00003[9] Jonathan Bishop. 2007. Increasing participation in online communities: A framework for human–computer interaction. Computers in Human Behavior 23 (072007), 1881–1893. https://doi.org/10.1016/j.chb.2005.11.004[10] Jacquelyn Burkell. 2006. Anonymity in Behavioural Research: Not Being Unnamed, But Being Unknown. University of Ottawa Law and Technology Journal 3(01 2006).
[11] Jie Cai and Donghee Yvette Wohn. 2019. Categorizing live streaming moderation
tools: An analysis of twitch. International Journal of Interactive Communication
Systems and Technologies (IJICST) 9, 2 (2019), 36–50.
[12] Jie Cai, Donghee Yvette Wohn, and Mashael Almoqbel. 2021. Moderation Visibility: Mapping the Strategies of Volunteer Moderators in Live Streaming Micro
Communities. In ACM International Conference on Interactive Media Experiences(Virtual Event, USA) (IMX ’21). Association for Computing Machinery, New York,NY, USA, 61–72. https://doi.org/10.1145/3452918.3458796[13] Eshwar Chandrasekharan, Shagun Jhaver, Amy Bruckman, and Eric Gilbert.
2022. Quarantined! Examining the Efects of a Community-Wide Moderation
Intervention on Reddit. ACM Trans. Comput.-Hum. Interact. 29, 4, Article 29 (mar2022), 26 pages. https://doi.org/10.1145/3490499[14] Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy
Bruckman, Clif Lampe, Jacob Eisenstein, and Eric Gilbert. 2018. The Internet’s
Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso,
and Macro Scales. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 32 (nov
2018), 25 pages. https://doi.org/10.1145/3274301[15] Weili Chen, Tuo Zhang, Zhiguang Chen, Zibin Zheng, and Yutong Lu. 2020.
Traveling the Token World: A Graph Analysis of Ethereum ERC20 Token
Ecosystem. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW’20). Association for Computing Machinery, New York, NY, USA, 1411–1421.
https://doi.org/10.1145/3366423.3380215[16] Haihan Duan, Jiaye Li, Sizheng Fan, Zhonghao Lin, Xiao Wu, and Wei Cai. 2021.
Metaverse for Social Good: A University Campus Prototype. In Proceedings ofthe 29th ACM International Conference on Multimedia (Virtual Event, China)(MM ’21). Association for Computing Machinery, New York, NY, USA, 153–161.
https://doi.org/10.1145/3474085.3479238[17] Youssef El Faqir, Javier Arroyo, and Samer Hassan. 2020. An Overview of Decentralized Autonomous Organizations on the Blockchain. In Proceedings of the 16th
International Symposium on Open Collaboration (Virtual conference, Spain) (OpenSym ’20). Association for Computing Machinery, New York, NY, USA, Article 11,
8 pages. https://doi.org/10.1145/3412569.3412579[18] Chris Elsden, Arthi Manohar, Jo Briggs, Mike Harding, Chris Speed, and John
Vines. 2018. Making Sense of Blockchain Applications: A Typology for HCI. In
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems(Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New
York, NY, USA, 1–14. https://doi.org/10.1145/3173574.3174032[19] Alessio Faccia, Narcisa Roxana Moşteanu, Luigi Pio Leonardo Cavaliere, andLeonardo Jose Mataruna-Dos-Santos. 2020. Electronic Money Laundering, The
Dark Side of Fintech: An Overview of the Most Recent Cases. In Proceedings of the2020 12th International Conference on Information Management and Engineering(Amsterdam, Netherlands) (ICIME 2020). Association for Computing Machinery,
New York, NY, USA, 29–34. https://doi.org/10.1145/3430279.3430284[20] Sizheng Fan, Tian Min, Xiao Wu, and Cai Wei. 2022. Towards understanding
governance tokens in liquidity mining: a case study of decentralized exchanges.
World Wide Web (2022), 1–20.
[21] Marcus Foth. 2017. The Promise of Blockchain Technology for Interaction Design.
In Proceedings of the 29th Australian Conference on Computer-Human Interaction(Brisbane, Queensland, Australia) (OZCHI ’17). Association for Computing Machinery, New York, NY, USA, 513–517. https://doi.org/10.1145/3152771.3156168

CHI ’23, April 23–28, 2023, Hamburg, Germany[22] Anna Fuste and Chris Schmandt. 2018. ARTextiles for Promoting Social Interactions Around Personal Interests. In Extended Abstracts of the 2018 CHI
Conference on Human Factors in Computing Systems (Montreal QC, Canada)(CHI EA ’18). Association for Computing Machinery, New York, NY, USA, 1–6.
https://doi.org/10.1145/3170427.3188589[23] Michel Grabisch, Alexis Poindron, and Agnieszka Rusinowska. 2019. A model ofanonymous infuence with anti-conformist agents. Journal of Economic Dynamics
and Control 109 (10 2019), 103773. https://doi.org/10.1016/j.jedc.2019.103773[24] Rong Han, Zheng Yan, Xueqin Liang, and Laurence T. Yang. 2022. How Can
Incentive Mechanisms and Blockchain Beneft with Each Other? A Survey. ACM
Comput. Surv. 55, 7, Article 136 (dec 2022), 38 pages. https://doi.org/10.1145/
3539604[25] Lars Hornuf, Theresa Kück, and Armin Schwienbacher. 2022. Initial coin oferings,
information disclosure, and fraud. Small Business Economics 58 (04 2022). https://doi.org/10.1007/s11187-021-00471-y[26] Manoel Horta Ribeiro, Shagun Jhaver, Savvas Zannettou, Jeremy Blackburn,Gianluca Stringhini, Emiliano De Cristofaro, and Robert West. 2021. Do Platform
Migrations Compromise Content Moderation? Evidence from rThe_Donald andrIncels. Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 316 (oct 2021),
24 pages. https://doi.org/10.1145/3476057[27] Michael E. Houle, Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur
Zimek. 2010. Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?. In Scientifc and Statistical Database Management, Michael Gertz and Bertram
Ludäscher (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 482–500.
[28] Eaman Jahani, Peter M. Kraft, Yoshihiko Suhara, Esteban Moro, and Alex Sandy
Pentland. 2018. ScamCoins, S*** Posters, and the Search for the Next BitcoinTM:
Collective Sensemaking in Cryptocurrency Discussions. Proc. ACM Hum.-Comput.
Interact. 2, CSCW, Article 79 (nov 2018), 28 pages. https://doi.org/10.1145/3274348[29] Shagun Jhaver, Iris Birman, Eric Gilbert, and Amy Bruckman. 2019. HumanMachine Collaboration for Content Regulation: The Case of Reddit Automoderator. ACM Trans. Comput.-Hum. Interact. 26, 5, Article 31 (jul 2019), 35 pages.
https://doi.org/10.1145/3338243[30] Shagun Jhaver, Christian Boylston, Diyi Yang, and Amy Bruckman. 2021. Evaluating the Efectiveness of Deplatforming as a Moderation Strategy on Twitter.
Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 381 (oct 2021), 30 pages.
https://doi.org/10.1145/3479525[31] Shagun Jhaver, Sucheta Ghoshal, Amy Bruckman, and Eric Gilbert. 2018. Online Harassment and Content Moderation: The Case of Blocklists. ACM Trans.
Comput.-Hum. Interact. 25, 2, Article 12 (mar 2018), 33 pages. https://doi.org/10.
1145/3185593[32] Yu Jiang, Tian Min, Sizheng Fan, Rongqi Tao, and Wei Cai. 2022. Towards
Understanding Player Behavior in Blockchain Games: A Case Study of Aavegotchi.
In Proceedings of the 17th International Conference on the Foundations of Digital
Games (Athens, Greece) (FDG ’22). Association for Computing Machinery, New
York, NY, USA, Article 21, 12 pages. https://doi.org/10.1145/3555858.3555883[33] Megan Knittel, Shelby Pitts, and Rick Wash. 2019. "The Most Trustworthy Coin":
How Ideological Tensions Drive Trust in Bitcoin. Proc. ACM Hum.-Comput.
Interact. 3, CSCW, Article 36 (nov 2019), 23 pages. https://doi.org/10.1145/3359138[34] Kevin Koidl. 2018. Towards Trust-Based Decentralized Ad-Hoc Social Networks.
In Companion Proceedings of the The Web Conference 2018 (Lyon, France) (WWW’18). International World Wide Web Conferences Steering Committee, Republic
and Canton of Geneva, CHE, 1545–1551. https://doi.org/10.1145/3184558.3191608[35] Merve Can Kus Khalilov and Albert Levi. 2018. A Survey on Anonymity andPrivacy in Bitcoin-Like Digital Cash Systems. IEEE Communications Surveys &Tutorials 20, 3 (2018), 2543–2585. https://doi.org/10.1109/COMST.2018.2818623[36] Jun Li, Alex Grintsvayg, Jeremy Kaufman, and Charles Fleming. 2020. LBRY:
A Blockchain-Based Decentralized Digital Content Marketplace. In 2020 IEEE
International Conference on Decentralized Applications and Infrastructures (DAPPS).
IEEE, Oxford, United Kingdom, 42–51. https://doi.org/10.1109/DAPPS49028.2020.
00005[37] Caitlin Lustig. 2019. Intersecting Imaginaries: Visions of Decentralized Autonomous Systems. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 210 (nov2019), 27 pages. https://doi.org/10.1145/3359312[38] David Meijer and Jolien Ubacht. 2018. The Governance of Blockchain Systems
from an Institutional Perspective, a Matter of Trust or Control?. In Proceedingsof the 19th Annual International Conference on Digital Government Research:
Governance in the Data Age (Delft, The Netherlands) (dg.o ’18). Association
for Computing Machinery, New York, NY, USA, Article 90, 9 pages. https://doi.org/10.1145/3209281.3209321[39] Tian Min and Wei Cai. 2022. Portrait of decentralized application users: an
overview based on large-scale Ethereum data. CCF Transactions on Pervasive
Computing and Interaction 4, 2 (2022), 124–141.
[40] Kelsie Nabben. 2021. Is a "Decentralized Autonomous Organization" a Panopticon? Algorithmic Governance as Creating and Mitigating Vulnerabilities in
DAOs. In Proceedings of the Interdisciplinary Workshop on (de) Centralizationin the Internet (Virtual Event, Germany) (IWCI’21). Association for Computing

Sizheng Fan, Tian Min, Xiao Wu, and Wei Cai

Machinery, New York, NY, USA, 18–25. https://doi.org/10.1145/3488663.3493791[41] Satoshi Nakamoto and A Bitcoin. 2008. A peer-to-peer electronic cash system.
Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf 4 (2008), 2.
[42] Hoang H. Nguyen, Dmytro Bozhkov, Zahra Ahmadi, Nhat-Minh Nguyen, andThanh-Nam Doan. 2022. SoChainDB: A Database for Storing and Retrieving
Blockchain-Powered Social Network Data. In Proceedings of the 45th International
ACM SIGIR Conference on Research and Development in Information Retrieval(Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York,NY, USA, 3036–3045. https://doi.org/10.1145/3477495.3531735[43] Lena Pellandini-Simanyi. 2020. The Financialization of Everyday Life. Taylor &Francis, England, 278–299.
[44] Leonardo F.R. Ribeiro, Pedro H.P. Saverese, and Daniel R. Figueiredo. 2017.
Struc2vec: Learning Node Representations from Structural Identity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining (Halifax, NS, Canada) (KDD ’17). Association for Computing Machinery, New York, NY, USA, 385–394. https://doi.org/10.1145/3097983.3098061[45] David Safo, Caglar Yildirim, Sara Di Bartolomeo, and Cody Dunne. 2020. Crowdsourcing Virtual Reality Experiments Using VRChat. In Extended Abstracts ofthe 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI,
USA) (CHI EA ’20). Association for Computing Machinery, New York, NY, USA,
1–8. https://doi.org/10.1145/3334480.3382829[46] Joseph Seering, Robert Kraut, and Laura Dabbish. 2017. Shaping Pro and AntiSocial Behavior on Twitch Through Moderation and Example-Setting. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work andSocial Computing (Portland, Oregon, USA) (CSCW ’17). Association for Computing Machinery, New York, NY, USA, 111–125. https://doi.org/10.1145/2998181.
2998277[47] Joseph Seering, Tony Wang, Jina Yoon, and Geof Kaufman. 2019. Moderator
engagement and community development in the age of algorithms. New Media &Society 21 (01 2019), 146144481882131. https://doi.org/10.1177/1461444818821316[48] Ben Shneiderman. 2011. Technology-Mediated Social Participation: The Next 25
Years of HCI Challenges. In Human-Computer Interaction. Design and Development
Approaches, Julie A. Jacko (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg,
3–14.
[49] Burton Speakman. 2019. Infuencing interaction: Does technology increase
public participation on community journalism websites? Newspaper Research Journal 40, 1 (2019), 38–50. https://doi.org/10.1177/0739532918814453
arXiv:https://doi.org/10.1177/0739532918814453[50] Melanie Swan. 2015. Blockchain: Blueprint for a new economy. "O’Reilly Media,
Inc.", Massachusetts.
[51] Lana Swartz. 2022. Theorizing the 2017 blockchain ICO bubble as a network
scam. New Media & Society 24 (07 2022), 1695–1713. https://doi.org/10.1177/
14614448221099224[52] Demival Vasques Filho and Dion RJ O’Neale. 2020. Transitivity and degree
assortativity explained: The bipartite structure of social networks. PhysicalReview E 101, 5 (2020), 052305.
[53] Friedhelm Victor and Bianca Katharina Lüders. 2019. Measuring EthereumBased ERC20 Token Networks. In Financial Cryptography and Data Security,
Ian Goldberg and Tyler Moore (Eds.). Springer International Publishing, Cham,
113–129.
[54] Vasillis Vlachokyriakos, Clara Crivellaro, Christopher A. Le Dantec, Eric Gordon,
Pete Wright, and Patrick Olivier. 2016. Digital Civics: Citizen Empowerment Withand Through Technology. In Proceedings of the 2016 CHI Conference Extended
Abstracts on Human Factors in Computing Systems (San Jose, California, USA) (CHI
EA ’16). Association for Computing Machinery, New York, NY, USA, 1096–1099.
https://doi.org/10.1145/2851581.2886436[55] Dejan Vujičić, Dijana Jagodić, and Siniša Ranđić. 2018. Blockchain technology,
bitcoin, and Ethereum: A brief overview. In 2018 17th International Symposium
INFOTEH-JAHORINA (INFOTEH). IEEE, East Sarajevo, Bosnia and Herzegovina,
1–6. https://doi.org/10.1109/INFOTEH.2018.8345547[56] Barry Wellman and Milena Gulia. 2018. Net-surfers don’t ride alone: Virtual
communities as communities. In Networks in the global village. Routledge, British,
331–366.
[57] Gavin Wood et al. 2014. Ethereum: A secure decentralised generalised transaction
ledger. Ethereum project yellow paper 151, 2014 (2014), 1–32.
[58] Lei Wu, Yufeng Hu, Yajin Zhou, Haoyu Wang, Xiapu Luo, Zhi Wang, Fan Zhang,and Kui Ren. 2021. Towards Understanding and Demystifying Bitcoin Mixing
Services. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW’21). Association for Computing Machinery, New York, NY, USA, 33–44. https://doi.org/10.1145/3442381.3449880[59] Shibing Zhou, Zhenyuan Xu, and Fei Liu. 2016. Method for determining the
optimal number of clusters based on agglomerative hierarchical clustering. IEEE
transactions on neural networks and learning systems 28, 12 (2016), 3007–3017.
[60] Weiqin Zou, David Lo, Pavneet Singh Kochhar, Xuan-Bach Dinh Le, Xin Xia,
Yang Feng, Zhenyu Chen, and Baowen Xu. 2021. Smart Contract Development:
Challenges and Opportunities. IEEE Transactions on Software Engineering 47, 10(2021), 2084–2106. https://doi.org/10.1109/TSE.2019.2942301