Our recent work “How Information Manipulation on Social Media Influences the NFT Investors’ Behavior: A Case Study of Goblintown.wtf” is accepted to be published in IEEE Transactions On Computational Social Systems. In this work, we analyze the participants, mechanism, and impact of Twitter information manipulation in the market. We also find five categories of investors in the NFT market under information manipulation.
Investors are keen to buy and use NFT (Non-Fungible Token) pictures as social media avatars and participate in online communities around NFT collections. However, information manipulation in the NFT market has led to investors significant losses. Our work explored a way to correspond social media accounts with Ethereum addresses and studied the microstructure of the NFT market.
Taking Goblintown.wtf as an example, we find there were four kinds of vital nodes involved in the information spreading on Twitter: the goblintown team, community core members, KOLs (Key Opinion Leaders), and counterfeit projects. The discussion topics in NFT communities were mainly related to rumors and “the fear of missing out” (FOMO) messages. Under information manipulation, the sentiment of the community members maintained fanatic.
Considering trading frequency, profits, and the posted amount of goblintown tweets, we cluster five categories of NFT investors: primary investors (low-frequency trading, low returns, and low tweets posted), amateur investors (low-frequency trading, low returns, and high tweets posted), fanatic investors (high-frequency trading, low returns, and high tweets posted), and rational investors. Among rational ones, we classified them as short-term rational investors (high-frequency trading, high returns, and low tweets posted), and long-term rational investors (low-frequency trading, high returns, and low tweets posted).
Our findings expand the investor classification under market information manipulation to the NFT market. We also explain the rational investors’ disappearance in the NFT community using behavioral finance theories.
This work is supported by Shenzhen Science and Technology Program, in part by the SeeDAO.