@article{10.1371/journal.pone.0208119,
    author = {Steinert, Lars AND Herff, Christian},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Predicting altcoin returns using social media},
    year = {2018},
    month = {12},
    volume = {13},
    url = {https://doi.org/10.1371/journal.pone.0208119},
    pages = {1-12},
    abstract = {Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called “altcoins” often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.},
    number = {12},
    url = {https://www.csl.uni-bremen.de/cms/images/documents/publications/Steinert_journal.pone.0208119.pdf},
    doi = {10.1371/journal.pone.0208119}
}