We study the problem of predicting the future performance of cryptocurrencies
using social media data. We propose a new model to measure the engagement of
users with topics discussed on social media based on interactions with social
media posts. This model overcomes the limitations of previous volume and
sentiment based approaches. We use this model to estimate engagement
coefficients for 48 cryptocurrencies created between 2019 and 2021 using data
from Twitter from the first month of the cryptocurrencies' existence. We find
that the future returns of the cryptocurrencies are dependent on the engagement
coefficients. Cryptocurrencies whose engagement coefficients are too low or too
high have lower returns. Low engagement coefficients signal a lack of interest,
while high engagement coefficients signal artificial activity which is likely
from automated accounts known as bots. We measure the amount of bot posts for
the cryptocurrencies and find that generally, cryptocurrencies with more bot
posts have lower future returns. While future returns are dependent on both the
bot activity and engagement coefficient, the dependence is strongest for the
engagement coefficient, especially for short-term returns. We show that simple
investment strategies which select cryptocurrencies with engagement
coefficients exceeding a fixed threshold perform well for holding times of a
few months.

研究加密货币市场中社交媒体数据对其价格预测的影响，提出一种测量用户对社交媒体上讨论的话题互动的新模型并根据此模型测算 48 种加密货币未来投资回报，结果表明加密货币的回报与其关注度及人为动态的自动化账户有关，而对具有一定阈值的加密货币进行选择的简单投资策略可在短期内获得较好收益。