# Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data

arXiv: https://arxiv.org/abs/2110.14317 · HTML: https://mlsanity.com/paper/2110.14317/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [STCN: Stochastic Temporal Convolutional Networks](https://mlsanity.com/paper/1902.06568/) (2019)
- [Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders](https://mlsanity.com/paper/1802.04065/) (2018)
- [MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price](https://mlsanity.com/paper/2105.00707/) (2021)
- [Social signals and algorithmic trading of Bitcoin](https://mlsanity.com/paper/1506.01513/) (2015)
- [Sensing Social Media Signals for Cryptocurrency News](https://mlsanity.com/paper/1903.11451/) (2019)
- [Evolutionary dynamics of the cryptocurrency market](https://mlsanity.com/paper/1705.05334/) (2017)
- [Inferring short-term volatility indicators from Bitcoin blockchain](https://mlsanity.com/paper/1809.07856/) (2018)

## Built on by (most cited first, 4 of 4)

- [PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin](https://mlsanity.com/paper/2206.00648/) (2022)
- [Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models](https://mlsanity.com/paper/2211.08281/) (2022)
- [Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility](https://mlsanity.com/paper/2202.08967/) (2022)
- [Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting](https://mlsanity.com/paper/2502.14897/) (2025)

## Abstract (arXiv)

Understanding the variations in trading price (volatility), and its response to exogenous information, is a well-researched topic in finance. In this study, we focus on finding stable and accurate volatility predictors for a relatively new asset class of cryptocurrencies, in particular Bitcoin, using deep learning representations of public social media data obtained from Twitter. For our experiments, we extracted semantic information and user statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute frequency price data over a horizon of 144 days. Using this data, we built several deep learning architectures that utilized different combinations of the gathered information. For each model, we conducted ablation studies to assess the influence of different components and feature sets over the prediction accuracy. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both classical autoregressive models and other deep learning-based architectures in the literature, and (ii) tweet author meta-information, even detached from the tweet itself, is a better predictor of volatility than the semantic content and tweet volume statistics. We demonstrate how different information sets gathered from social media can be utilized in different architectures and how they affect the prediction results. As an additional contribution, we make our dataset public for future research.