Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
Milka Trajkova,
A’aeshah Alhakamy,
Francesco Cafaro,
Sanika Vedak,
Rashmi Mallappa,
Sreekanth R. Kankara
Affiliations
Milka Trajkova
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
A’aeshah Alhakamy
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Francesco Cafaro
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Sanika Vedak
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Rashmi Mallappa
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
Sreekanth R. Kankara
Department of Human-Centered Computing, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
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.
