# Sensing Social Media Signals for Cryptocurrency News

arXiv: https://arxiv.org/abs/1903.11451 · HTML: https://mlsanity.com/paper/1903.11451/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data](https://mlsanity.com/paper/2110.14317/) (2021)
- [Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models](https://mlsanity.com/paper/2206.06320/) (2022)

## Abstract (arXiv)

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track relevant news in real-time, we (i) match news from the web with tweets from social media, (ii) track their intraday tweet activity and (iii) explore different machine learning models for predicting the number of the article mentions on Twitter within the first 24 hours after its publication. We compare several machine learning models, such as linear extrapolation, linear and random forest autoregressive models, and a sequence-to-sequence neural network. We find that the random forest autoregressive model behaves comparably to more complex models in the majority of tasks.