"Mining Tweets to Predict Future Bitcoin Price"

Mining millions of tweets reveals hidden patterns in Bitcoin price movements through AI

This paper analyzes 16M Bitcoin-related tweets to predict cryptocurrency price movements using sentiment analysis, clustering, and machine learning models. The research combines social media sentiment with price data to understand and forecast Bitcoin price fluctuations.

https://arxiv.org/abs/2412.02148

🎯 Original Problem:

Cryptocurrency markets are highly volatile, with prices significantly influenced by social media sentiment. Traditional price prediction methods struggle to capture this social influence.

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🔬 Solution in this Paper:

→ Analyzes 16M Bitcoin-related tweets using language detection and sentiment analysis.

→ Aggregates daily tweet metrics including likes, replies, retweets, and sentiment scores.

→ Employs clustering to identify user groups affecting price differently.

→ Uses regression models (Linear, Ridge, Lasso) and classification models (Random Forest, XGBoost) for price prediction.

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💡 Key Insights:

→ Most Bitcoin tweets (90%) are neutral, with 7% positive and 3% negative

→ Bitcoin-related tweet volume peaks during work hours (9 AM - 5 PM)

→ Friday shows highest tweet activity among weekdays

→ User clustering reveals three distinct groups affecting price differently

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📊 Results:

→ Random Forest classifier achieves 62% accuracy in predicting price direction

→ Ridge regression performs best among prediction models

→ F1-score of 75% for price movement classification

→ Sentiment correlation with price: Positive (0.13), Negative (0.15), Neutral (0.07)