@inproceedings{pawlicka-maule-johnson-2020-using,
title = "Using Social Media For Bitcoin Day Trading Behavior Prediction",
author = "Pawlicka Maule, Anna Paula and
Johnson, Kristen",
editor = "Cunha, Rossana and
Shaikh, Samira and
Varis, Erika and
Georgi, Ryan and
Tsai, Alicia and
Anastasopoulos, Antonios and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
month = jul,
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.winlp-1.37/",
doi = "10.18653/v1/2020.winlp-1.37",
pages = "140--143",
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."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pawlicka-maule-johnson-2020-using">
<titleInfo>
<title>Using Social Media For Bitcoin Day Trading Behavior Prediction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anna</namePart>
<namePart type="given">Paula</namePart>
<namePart type="family">Pawlicka Maule</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kristen</namePart>
<namePart type="family">Johnson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Widening Natural Language Processing Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rossana</namePart>
<namePart type="family">Cunha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samira</namePart>
<namePart type="family">Shaikh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erika</namePart>
<namePart type="family">Varis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ryan</namePart>
<namePart type="family">Georgi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alicia</namePart>
<namePart type="family">Tsai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antonios</namePart>
<namePart type="family">Anastasopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khyathi</namePart>
<namePart type="given">Raghavi</namePart>
<namePart type="family">Chandu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Seattle, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">pawlicka-maule-johnson-2020-using</identifier>
<identifier type="doi">10.18653/v1/2020.winlp-1.37</identifier>
<location>
<url>https://aclanthology.org/2020.winlp-1.37/</url>
</location>
<part>
<date>2020-07</date>
<extent unit="page">
<start>140</start>
<end>143</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Using Social Media For Bitcoin Day Trading Behavior Prediction
%A Pawlicka Maule, Anna Paula
%A Johnson, Kristen
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Varis, Erika
%Y Georgi, Ryan
%Y Tsai, Alicia
%Y Anastasopoulos, Antonios
%Y Chandu, Khyathi Raghavi
%S Proceedings of the Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F pawlicka-maule-johnson-2020-using
%X 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.
%R 10.18653/v1/2020.winlp-1.37
%U https://aclanthology.org/2020.winlp-1.37/
%U https://doi.org/10.18653/v1/2020.winlp-1.37
%P 140-143
Markdown (Informal)
[Using Social Media For Bitcoin Day Trading Behavior Prediction](https://aclanthology.org/2020.winlp-1.37/) (Pawlicka Maule & Johnson, WiNLP 2020)
ACL