# Sensing Social Media Signals for Cryptocurrency News

## TL;DR

This paper presents a method to track and predict cryptocurrency news mentions on Twitter in real-time by matching web news with social media data and applying various machine learning models, including random forest autoregressive models.

## Contribution

It introduces a comprehensive approach combining news matching, activity tracking, and predictive modeling for cryptocurrency news on social media, with a comparison of multiple machine learning techniques.

## Key findings

- Random forest autoregressive models perform comparably to complex models.
- The approach effectively tracks and predicts Twitter mentions of cryptocurrency news.
- Different machine learning models have varying effectiveness in real-time social media monitoring.

## Abstract

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.