Sensing Social Media Signals for Cryptocurrency News
This work addresses real-time news tracking for cryptocurrency audiences, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of predicting cryptocurrency news popularity on Twitter by matching news with tweets and using machine learning models, finding that a random forest autoregressive model performed comparably to more complex models in most tasks.
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.