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.

通过引入基于用户声誉的 ' 液态民主 ' 方法，本文提出了一种分析比特币在 Twitter 上的趋势的新模型，旨在识别对比特币价格和交易量产生最大影响的趋势，以及它们的影响力。该模型使用了基于声誉评级系统的 Twitter 情感分析模型，以确定对比特币价格变化和交易量的影响。此外，声誉模型还考虑了社交网络上用户的高序好友（在本案例研究中为初始 Twitter 输入渠道），以提高声誉结果的准确性和多样性。通过分析 Twitter 上与比特币有关的新闻，我们了解趋势和用户情感如何通过我们的液态声誉评级系统来影响研究时间范围内的比特币价格波动和交易活动。该声誉模型也可以作为其他趋势和情感分析模型的附加层面。本文还提出了液态声誉模型的实施、挑战和未来范围。