标题： 用于加密货币新闻的感知社交媒体信号 显示英文标题

标题： Sensing Social Media Signals for Cryptocurrency News

摘要： 实时跟踪和监控相关和重要的新闻在多个工业部门中具有至关重要的兴趣。 在本工作中，我们专注于加密货币新闻集，这些新闻最近引起了普通公众和金融受众的广泛关注。 为了实时跟踪相关新闻，我们（i）将网络上的新闻与社交媒体上的推文进行匹配，（ii）跟踪其日内推文活动，并（iii）探索不同的机器学习模型以预测文章在发布后24小时内被提及的次数。 我们比较了几种机器学习模型，如线性外推、线性和随机森林自回归模型，以及一个序列到序列的神经网络。 我们发现，在大多数任务中，随机森林自回归模型的表现与更复杂的模型相当。 显示英文摘要

摘要： 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.