Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin

TL;DR

This paper introduces a liquid rank reputation system for Twitter trend analysis related to Bitcoin, aiming to identify impactful trends and their influence on Bitcoin prices and trading volume using sentiment analysis and social network data.

Contribution

It presents a novel reputation-based model incorporating higher-order social connections to improve trend impact analysis on Bitcoin markets.

Findings

Reputation system improves trend impact detection accuracy

User sentiment correlates with Bitcoin price fluctuations

Higher-order social connections enhance reputation assessment

Abstract

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.