Have a personal or library account? Click to login
Digital Narratives of COVID-19: A Twitter Dataset for Text Analysis in Spanish Cover

Digital Narratives of COVID-19: A Twitter Dataset for Text Analysis in Spanish

Open Access
|Jun 2021

References

  1. 1Abdo, M. S., Alghonaim, A. S., & Essam, B. A. (2020). Public perception of COVID-19’s global health crisis on Twitter until 14 weeks after the outbreak. Digital Scholarship in the Humanities, fqaa037. DOI: 10.1093/llc/fqaa037
  2. 2Allés-Torrent, S. (2020). A Twitter Dataset for Digital Narratives. Digital Narratives of COVID-19. Published May 23, 2020. Retrieved April 05, 2021 from https://covid.dh.miami.edu/2020/05/23/twitter-dataset-for-digital-narratives/
  3. 3Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., Artemova, K., Tutubalina, E., & Chowell, G. (2021). A large-scale COVID-19 Twitter chatter dataset for open scientific research—An international collaboration [Data set]. Zenodo. DOI: 10.5281/zenodo.4460047
  4. 4Chew, C., & Eysenbach, G. (2010). Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS One, 5(11). DOI: 10.1371/journal.pone.0014118
  5. 5Documenting the now. (n.d.). Retrieved April 05, 2021, from https://www.docnow.io/
  6. 6Ferrara, E. (2020). What types of COVID-19 conspiracies are populated by Twitter bots? First Monday, 25(6). DOI: 10.5210/fm.v25i6.10633
  7. 7Fu, K. W., Liang, H., Saroha, N., Tse, Z. T. H., Ip, P., & Fung, I. C. H. (2016). How people react to Zika virus outbreaks on Twitter? A computational content analysis. American Journal of Infection Control, 44(12), 17001702. DOI: 10.1016/j.ajic.2016.04.253
  8. 8Gelfgren, S. (2016). Reading Twitter: Combining Qualitative and Quantitative Methods in the Interpretation of Twitter Material. In G. Griffin (Ed.), Research Methods for Reading Digital Data in the Digital Humanities (pp. 93100). Edinburgh: Edinburgh University Press.
  9. 9Grandjean, M. (2016). A Social Network Analysis of Twitter: Mapping the Digital Humanities Community. Cogent Arts & Humanities, 3(1). DOI: 10.1080/23311983.2016.1171458
  10. 10Jiang, J., Chen, E., Yan, S., Lerman, K., & Ferrara, E. (2020). Political Polarization Drives Online Conversations about COVID-19 in the United States. Human Behavior and Emerging Technologies, 2(3), 200211. DOI: 10.1002/hbe2.202
  11. 11Jockers, M., & Underwood, T. (2016). Text-Mining the Humanities. In S. Schreibman, R. Siemens & J. Unsworth (Eds.), A New Companion to Digital Humanities (pp. 291306). New York: John Wiley & Sons. DOI: 10.1002/9781118680605.ch20
  12. 12Kerchner, D., & Wrubel, L. (2020). Coronavirus Tweet Ids [Data set]. Harvard Dataverse. DOI: 10.7910/DVN/LW0BTB
  13. 13Lamsal, R. (2020). Coronavirus (COVID-19) Tweets Dataset [Data set]. IEEE. DOI: 10.21227/781w-ef42
  14. 14Quan-Haase, A., Martin, K., & McCay-Peet, L. (2015). Networks of Digital Humanities Scholars: The Informational and Social Uses and Gratifications of Twitter. Big Data & Society, 2(1). DOI: 10.1177/2053951715589417
  15. 15Sinclair, S., & Rockwell, G. (2016). Text Analysis and Visualization: Making Meaning Count. In S. Schreibman, R. Siemens & J. Unsworth (Eds.), A New Companion to Digital Humanities (pp. 274290). New York: John Wiley & Sons. DOI: 10.1002/9781118680605.ch19
  16. 16Williams, S. A., Terras, M., & Warwick, C. (2013). What do people study when they study Twitter? Classifying Twitter related academic papers. Journal of Documentation, 69(3). DOI: 10.1108/JD-03-2012-0027
DOI: https://doi.org/10.5334/johd.28 | Journal eISSN: 2059-481X
Language: English
Published on: Jun 10, 2021
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Susanna Allés-Torrent, Gimena del Rio Riande, Jerry Bonnell, Dieyun Song, Nidia Hernández, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.