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.

该研究旨在实时追踪并监测加密货币新闻，利用机器学习模型预测其发布后前 24 小时在 Twitter 上被提及的数量，并比较了多种模型，其结果表明随机森林自回归模型在大多数情况下表现良好。

利用社交媒体信号感知加密货币新闻

Sensing Social Media Signals for Cryptocurrency News

Clustering news across languages enables efficient media monitoring by
aggregating articles from multilingual sources into coherent stories. Doing so
in an online setting allows scalable processing of massive news streams. To
this end, we describe a novel method for clustering an incoming stream of
multilingual documents into monolingual and crosslingual story clusters. Unlike
typical clustering approaches that consider a small and known number of labels,
we tackle the problem of discovering an ever growing number of cluster labels
in an online fashion, using real news datasets in multiple languages. Our
method is simple to implement, computationally efficient and produces
state-of-the-art results on datasets in German, English and Spanish.

该研究提出一种基于多语言文档流聚类的方法，可用于实时新闻监测和处理，并在德语、英语和西班牙语数据集上实现了最优结果。