Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
1
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
2
Department of Computer Science, School of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Informatics 2020, 7(3), 35; https://doi.org/10.3390/informatics7030035
Received: 7 August 2020
/
Revised: 2 September 2020
/
Accepted: 10 September 2020
/
Published: 15 September 2020
(This article belongs to the Special Issue Feature Papers in Big Data)
Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.
View Full-Text
Keywords:
COVID-19; Coronavirus; sars-cov-2; data visualization; social media; twitter; casual visualizations; temporal reasoning; proportional reasoning; human-data interaction; design
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
MDPI and ACS Style
Trajkova, M.; Alhakamy, A.; Cafaro, F.; Vedak, S.; Mallappa, R.; Kankara, S.R. Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges. Informatics 2020, 7, 35. https://doi.org/10.3390/informatics7030035
AMA Style
Trajkova M, Alhakamy A, Cafaro F, Vedak S, Mallappa R, Kankara SR. Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges. Informatics. 2020; 7(3):35. https://doi.org/10.3390/informatics7030035
Chicago/Turabian StyleTrajkova, Milka, A’aeshah Alhakamy, Francesco Cafaro, Sanika Vedak, Rashmi Mallappa, and Sreekanth R. Kankara. 2020. "Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges" Informatics 7, no. 3: 35. https://doi.org/10.3390/informatics7030035
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
