Using Social Media For Bitcoin Day Trading Behavior Prediction
Abstract
This abstract presents preliminary work in the application of natural language processing techniques and social network modeling for the prediction of cryptocurrency trading and investment behavior. Specifically, we are building models to use language and social network behaviors to predict if the tweets of a 24-hour period can be used to buy or sell cryptocurrency to make a profit. In this paper we present our novel task and initial language modeling studies.- Anthology ID:
- 2020.winlp-1.37
- Volume:
- Proceedings of the Fourth Widening Natural Language Processing Workshop
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, USA
- Editors:
- Rossana Cunha, Samira Shaikh, Erika Varis, Ryan Georgi, Alicia Tsai, Antonios Anastasopoulos, Khyathi Raghavi Chandu
- Venue:
- WiNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 140–143
- Language:
- URL:
- https://aclanthology.org/2020.winlp-1.37
- DOI:
- 10.18653/v1/2020.winlp-1.37
- Cite (ACL):
- Anna Paula Pawlicka Maule and Kristen Johnson. 2020. Using Social Media For Bitcoin Day Trading Behavior Prediction. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 140–143, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Using Social Media For Bitcoin Day Trading Behavior Prediction (Pawlicka Maule & Johnson, WiNLP 2020)