Zika discourse in the Americas: A multilingual topic analysis of Twitter
Abstract
Article Authors Metrics Comments Media Coverage Abstract Introduction Materials and methods Results Discussion Acknowledgments References Reader Comments (0) Media Coverage (0) Figures Abstract This work examines Twitter discussion surrounding the 2015 outbreak of Zika, a virus that is most often mild but has been associated with serious birth defects and neurological syndromes. We introduce and analyze a collection of 3.9 million tweets mentioning Zika geolocated to North and South America, where the virus is most prevalent. Using a multilingual topic model, we automatically identify and extract the key topics of discussion across the dataset in English, Spanish, and Portuguese. We examine the variation in Twitter activity across time and location, finding that rises in activity tend to follow to major events, and geographic rates of Zika-related discussion are moderately correlated with Zika incidence (ρ = .398).
- Authors:
-
- Univ. of Pittsburgh, PA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of Colorado, Boulder, CO (United States)
- Univ. of Maryland, College Park, MD (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1526952
- Report Number(s):
- LA-UR-18-25885
Journal ID: ISSN 1932-6203
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 14; Journal Issue: 5; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; Biological Science; Information Science
Citation Formats
Pruss, Dasha, Daughton, Ashlynn Rae, Paul, Michael J., Fujinuma, Yoshinari, Arnot, Brad, Szafir, Danielle Albers, and Boyd-Graber, Jordan. Zika discourse in the Americas: A multilingual topic analysis of Twitter. United States: N. p., 2019.
Web. doi:10.1371/journal.pone.0216922.
Pruss, Dasha, Daughton, Ashlynn Rae, Paul, Michael J., Fujinuma, Yoshinari, Arnot, Brad, Szafir, Danielle Albers, & Boyd-Graber, Jordan. Zika discourse in the Americas: A multilingual topic analysis of Twitter. United States. https://doi.org/10.1371/journal.pone.0216922
Pruss, Dasha, Daughton, Ashlynn Rae, Paul, Michael J., Fujinuma, Yoshinari, Arnot, Brad, Szafir, Danielle Albers, and Boyd-Graber, Jordan. Thu .
"Zika discourse in the Americas: A multilingual topic analysis of Twitter". United States. https://doi.org/10.1371/journal.pone.0216922. https://www.osti.gov/servlets/purl/1526952.
@article{osti_1526952,
title = {Zika discourse in the Americas: A multilingual topic analysis of Twitter},
author = {Pruss, Dasha and Daughton, Ashlynn Rae and Paul, Michael J. and Fujinuma, Yoshinari and Arnot, Brad and Szafir, Danielle Albers and Boyd-Graber, Jordan},
abstractNote = {Article Authors Metrics Comments Media Coverage Abstract Introduction Materials and methods Results Discussion Acknowledgments References Reader Comments (0) Media Coverage (0) Figures Abstract This work examines Twitter discussion surrounding the 2015 outbreak of Zika, a virus that is most often mild but has been associated with serious birth defects and neurological syndromes. We introduce and analyze a collection of 3.9 million tweets mentioning Zika geolocated to North and South America, where the virus is most prevalent. Using a multilingual topic model, we automatically identify and extract the key topics of discussion across the dataset in English, Spanish, and Portuguese. We examine the variation in Twitter activity across time and location, finding that rises in activity tend to follow to major events, and geographic rates of Zika-related discussion are moderately correlated with Zika incidence (ρ = .398).},
doi = {10.1371/journal.pone.0216922},
journal = {PLoS ONE},
number = 5,
volume = 14,
place = {United States},
year = {Thu May 23 00:00:00 EDT 2019},
month = {Thu May 23 00:00:00 EDT 2019}
}
Web of Science
Figures / Tables:
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