Abstract—Initial Coin Offering (ICO) is a fundraising method utilized by blockchain startups to raise capital by issuing and selling digital tokens to investors. ICOs have become widely popular for cryptocurrency fundraising, often generating millions of dollars, and surpassing traditional crowdfunding methods like Initial Public Offerings. However, ICO is a risky way of investing and raising capital due to the lack of regulations and standardisation. In this research, we delve into the impact of social media and sentiment analysis on the success of ICOs, employing various machine learning models and Large Language Models. Our analysis is based on data from over 1,000 ICOs gathered from diverse ICO information platforms, coupled with a corpus of 910,478 tweets associated with these ICOs. We extend our investigation to include other social media platforms such as BitcoinTalk, Telegram, Facebook, and Medium. Our analysis revealed that valuable insights regarding the success of ICOs can be derived by examining text sentiment and investigating metadata across these diverse social media channels.
 
Keywords—token sales, web scraping, sentiment analysis, social media, Bidirectional Encoder Representations from Transformers (BERT)


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