Understanding NFT Price Moves through Social Media Keywords Analysis

Junliang Luo, Yongzheng Jia, Xue Liu

Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly. The overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT social media communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for more than half of the 19 top authentic NFT projects but seldom copycat projects. Besides, to investigate the price moves predictability using social media features, we conduct an experiment of predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given social-media-extracted word features and interpret the feature importance to find insights into the NFT communities. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy above the random baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves.

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