TY - GEN

T1 - Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin

AU - Saxena, Abhishek

AU - Kolonin, Anton

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - liquid democracy

KW - peer-to-peer systems

KW - recommendation systems

KW - reputation systems

KW - sentiment analysis

KW - social computing

KW - trend analysis

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85199139724&origin=inward&txGid=fb4b6a387a9653758465a0ffe769fb93

UR - https://www.mendeley.com/catalogue/a568b247-8cc8-39c7-be3c-5aad628244cd/

U2 - 10.1109/USBEREIT61901.2024.10584003

DO - 10.1109/USBEREIT61901.2024.10584003

M3 - Conference contribution

SN - 9798350362893

T3 - Proceedings - 2024 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2024

SP - 300

EP - 303

BT - Proceedings - 2024 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2024

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2024 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology

Y2 - 13 May 2024 through 15 May 2024

ER -