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 cryptocurrency news, which recently became of emerging interest to the general and financial audience. In order to track popular 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 article mentions on Twitter 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000347635Publication status
publishedExternal links
Book title
Proceedings of the 2019 World Wide Web Conference (WWW’19)Pages / Article No.
Publisher
Association for Computing MachineryEvent
Subject
Social media; Mining and learning; CryptocurrencyOrganisational unit
03784 - Helbing, Dirk / Helbing, Dirk
09588 - Zhang, Ce (ehemalig) / Zhang, Ce (former)
Funding
654024 - SoBigData Research Infrastructure (SBFI)
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