  Cryptocurrency is a fast-moving space, with a continuous influx of new
projects every year. However, an increasing number of incidents in the space,
such as hacks and security breaches, threaten the growth of the community and
the development of technology. This dynamic and often tumultuous landscape is
vividly mirrored and shaped by discussions within Crypto Twitter, a key digital
arena where investors, enthusiasts, and skeptics converge, revealing real-time
sentiments and trends through social media interactions. We present our
analysis on a Twitter dataset collected during a formative period of the
cryptocurrency landscape. We collected 40 million tweets using
cryptocurrency-related keywords and performed a nuanced analysis that involved
grouping the tweets by semantic similarity and constructing a tweet and user
network. We used sentence-level embeddings and autoencoders to create K-means
clusters of tweets and identified six groups of tweets and their topics to
examine different cryptocurrency-related interests and the change in sentiment
over time. Moreover, we discovered sentiment indicators that point to real-life
incidents in the crypto world, such as the FTX incident of November 2022. We
also constructed and analyzed different networks of tweets and users in our
dataset by considering the reply and quote relationships and analyzed the
largest components of each network. Our networks reveal a structure of bot
activity in Crypto Twitter and suggest that they can be detected and handled
using a network-based approach. Our work sheds light on the potential of social
media signals to detect and understand crypto events, benefiting investors,
regulators, and curious observers alike, as well as the potential for bot
detection in Crypto Twitter using a network-based approach.
