Individual users on social media platforms like Twitter can significantly influence highly volatile assets, including cryptocurrencies. However, current research has overlooked this aspect, focusing on sentiment analysis that includes all posts from all users. Making it challenging to detect trends caused by individuals. To address this gap, we introduce the Asset Influence Score (AIS), a percentage-based metric that assesses the likelihood of a newly issued tweet aligning with periods of heightened trading activity. By analyzing price data and tweets concurrently, we identify correlations that enable to predict the likelihood of specific users' tweets co-occurring with increased trading activity. Evaluating the AIS using a publicly available prototype and Twitter data from 2020 to 2023, we find that using the AIS as a buy signal outperforms buy-and-hold and technical trading strategies while maintaining high liquidity. Demonstrating the applicability of AIS in improving trading decisions and identifying key individuals on social media platforms.

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