Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin
- URL: http://arxiv.org/abs/2406.17904v1
- Date: Tue, 25 Jun 2024 19:35:25 GMT
- Title: Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin
- Authors: Abhishek Saxena, Anton Kolonin,
- Abstract summary: This paper proposes a new model for analyzing Bitcoin trends on Twitter by incorporating a 'liquid democracy' approach based on user reputation.
It uses a Twitter sentiment analysis model based on a reputation rating system to determine the impact on Bitcoin price change and traded volume.
- Score: 0.23020018305241333
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Cryptocurrency Price Prediction using Twitter Sentiment Analysis [0.0]
This study seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin.
We develop an end-to-end model that can forecast the sentiment of a set of tweets and forecast the price of Bitcoin.
The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data.
arXiv Detail & Related papers (2023-03-03T18:42:01Z) - Evaluating Impact of Social Media Posts by Executives on Stock Prices [0.5429166905724048]
Social media like Twitter, Reddit have become hotspots of such influences.
This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts.
arXiv Detail & Related papers (2022-11-01T03:45:17Z) - PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme
price movement prediction of Bitcoin [8.38397409405955]
We propose a multimodal model for predicting extreme price fluctuations in Bitcoin.
This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content.
We show that it can be used to build a profitable trading strategy with a reduced risk over a hold' or moving average strategy.
arXiv Detail & Related papers (2022-05-30T19:25:12Z) - A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools
Stock Prediction [100.9772316028191]
In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models.
Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation.
arXiv Detail & Related papers (2022-05-01T05:12:22Z) - Identification of Twitter Bots based on an Explainable ML Framework: the
US 2020 Elections Case Study [72.61531092316092]
This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data.
Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm.
Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
arXiv Detail & Related papers (2021-12-08T14:12:24Z) - Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data [2.9223917785251285]
We focus on volatility predictions for a relatively new asset class of cryptocurrencies (in particular, Bitcoin) using deep learning representations of public social media data from Twitter.
For the field work, we extracted semantic information and user interaction statistics from over 30 million Bitcoin-related tweets.
We built several deep learning architectures that utilized a combination of the gathered information.
arXiv Detail & Related papers (2021-10-27T09:55:03Z) - News consumption and social media regulations policy [70.31753171707005]
We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation.
Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content.
The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
arXiv Detail & Related papers (2021-06-07T19:26:32Z) - The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations [50.521292491613224]
This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump.
The pump and dump scheme is a fraud as old as the stock market. Now, it got new vitality in the loosely regulated market of cryptocurrencies.
We report on three case studies related to pump and dump groups.
arXiv Detail & Related papers (2021-05-03T10:20:47Z) - Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
We argue that it is critical to understand what user attributes potentially cause users to share fake news.
In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities.
We propose a principled approach to alleviating selection bias in fake news dissemination.
arXiv Detail & Related papers (2020-10-20T19:37:04Z) - Real-Time Prediction of BITCOIN Price using Machine Learning Techniques
and Public Sentiment Analysis [0.0]
The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis.
Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment.
We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts.
arXiv Detail & Related papers (2020-06-18T15:40:11Z) - Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations [50.521292491613224]
We perform an in-depth analysis of pump and dump schemes organized by communities over the Internet.
We observe how these communities are organized and how they carry out the fraud.
We introduce an approach to detect the fraud in real time that outperforms the current state of the art.
arXiv Detail & Related papers (2020-05-04T21:36:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.