  Analyzing social media trends can create a win-win situation for both
creators and consumers. Creators can receive fair compensation, while consumers
gain access to engaging, relevant, and personalized content. This paper
proposes a new model for analyzing Bitcoin trends on Twitter by incorporating a
'liquid democracy' approach based on user reputation. This system aims to
identify the most impactful trends and their influence on Bitcoin prices and
trading volume. It uses a Twitter sentiment analysis model based on a
reputation rating system to determine the impact on Bitcoin price change and
traded volume. In addition, the reputation model considers the users'
higher-order friends on the social network (the initial Twitter input channels
in our case study) to improve the accuracy and diversity of the reputation
results. We analyze Bitcoin-related news on Twitter to understand how trends
and user sentiment, measured through our Liquid Rank Reputation System, affect
Bitcoin price fluctuations and trading activity within the studied time frame.
This reputation model can also be used as an additional layer in other trend
and sentiment analysis models. The paper proposes the implementation,
challenges, and future scope of the liquid rank reputation model.
