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Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data

This repository contains (part of) the code, document and references for the paper 'Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data' by M. Eren Akbiyik, Mert Erkul, Killian Kämpf, Dr. Vaiva Vasiliauskaite and Dr. Nino Antulov-Fantulin at ETH Zürich. This work is published in the Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM '23).

Dataset

The Tweet dataset parsed and used as part of this work is publicly available here under the license CC BY 4.0. The dataset contains 30 million cryptocurrency-related tweets from 10.10.2020 to 3.3.2021.

Citation

If you use this code or the dataset, please cite our paper.

ACM Reference Format

M. Eren Akbiyik, Mert Erkul, Killian Kämpf, Vaiva Vasiliauskaite, and Nino Antulov-Fantulin. 2023. Ask “Who”, Not “What”: Bitcoin Volatility Forecasting with Twitter Data. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM ’23), February 27–March 3, 2023, Singapore, Singapore. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3539597.3570387

BibTeX

@inproceedings{10.1145/3539597.3570387,
    author = {Akbiyik, M. Eren and Erkul, Mert and K{\"a}mpf, Killian and Vasiliauskaite, Vaiva and Antulov-Fantulin, Nino},
    title = {Ask “Who”, Not “What”: Bitcoin Volatility Forecasting with Twitter Data},
    year = {2023},
    isbn = {978-1-4503-9407-9/23/02},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3539597.3570387},
    doi = {10.1145/3539597.3570387},
    booktitle = {Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining},
    pages = {9},
    numpages = {9},
    keywords = {Bitcoin, Twitter, Volatility Forecasting},
    location = {Singapore, Singapore},
    series = {WSDM ’23}
}

Acknowledgements

M.E.A., M.E. and K.K. thank Prof. Dr. Ce Zhang for their help during this research, Benjamin Suter for his help on the collected tweet dataset, and the the ETH Zürich DS3Lab for giving us access to their computer infrastructure. N.A.F. and V.V. are supported by the European Union - Horizon 2020 Program under the scheme 'INFRAIA-01-2018-2019 - Integrating Activities for Advanced Communities', Grant Agreement no. 871042, 'SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics' (http://www.sobigdata.eu).