Deciphering Crypto Twitter

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Kang, Inwon
Ahmed Mridul, Maruf
Sanders, Abraham
Ma, Yao
Munasinghe, Thilanka
Gupta, Aparna
Seneviratne, Oshani
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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 keywords related to cryptocurrency 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. We identified six groups of tweets and their topics to examine different cryptocurrency-related interests and the change in sentiment over time. For example, we identified different groups of tweets demonstrating coordinated behavior in the market or expressing distrust in centralized cryptocurrency exchanges. 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.
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Inwon Kang, Maruf Ahmed Mridul, Abraham Sanders, Yao Ma, Thilanka Munasinghe, Aparna Gupta, and Oshani Seneviratne. 2024. Deciphering Crypto Twitter. In Proceedings of the 16th ACM Web Science Conference (WEBSCI '24). Association for Computing Machinery, New York, NY, USA, 331–342.
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