Purpose This paper aims to review and critically assess the role that data visualizations played as communication media tools to help society during a worldwide crisis. This paper re-creates and analyzes several visualizations, critically and ethically assesses their strengths and limitations and provides a set of best practices that are informative, accurate, ethical and engaging at each stage in a reader’s interest. Design/methodology/approach The paper bases its methodology on the construct of “The Network Society” (Van Dijk, 2006; Castells, 2000, 2006) (...) by creating a series of social networked visualizations, identifying the challenges and pitfalls associated with this communication approach and suggesting best practices in information communication technology. The case study is COVID-19. Findings The research in this study found that visual data dashboards and interactive Web-based charts did play a significant role in helping society understand COVID-19’s impact to make better informed decisions about society’s health and safety. Research limitations/implications Visual expositions of data do have strengths and weaknesses depending on how they are designed, how they communicate the story and how they are ethically deployed. Best practices are provided to help mitigate these limitations. Practical implications Visualizations are certainly not new, but the technology for rapidly developing and sharing them is new. Visual expositions provide an effective media for communicating complex information to a networked society. Social implications Visual expositions provide an effective media for communicating complex information to a networked society. Originality/value This paper highlights the significance of the need to understand complex data in a crisis in a visual format and to communicate the information quickly, persuasively, effectively and ethically to a networked audience. (shrink)

As the coronavirus pandemic continues apace in the United States, the dizzying amount of data being generated, analyzed and consumed about the virus has led to calls to proclaim this the first ‘data-driven pandemic’. But at the same time, it seems that this plethora of data has not meant a better grasp on the reality of the pandemic and its effects. Even as we have the potential to digitally track and trace nearly every single individual who has contracted the virus, (...) we have no idea exactly how many people have had the virus, been hospitalized, or died because of it, largely due to a confluence of factors, particularly active obfuscation and mismanagement by public authorities and misinformation spread through social media and right-wing media channels. But beyond these dynamics, there also lies the less nefarious ways that the everyday, subjective practices of data collection, analysis and visualization have the potential to themselves produce these very same dynamics where data is at once valorized and ignored, preeminent and completely useless. That is, the pandemic has revealed only the general inadequacy of our data infrastructures and assemblages to solving pressing social issues, but also the more general shift towards a ‘post-truth’ disposition in contemporary social life. But, as this paper argues, it would be a mistake to see the centrality of data as being somehow the opposite from the larger post-truth apparatus, as the two are instead fundamentally intertwined and co-produced. (shrink)

Public discourse about the COVID-19 that appears on Twitter and other social media platforms provides useful insights into public concerns and responses to the pandemic. However, acknowledging that public discourse around COVID-19 is multi-faceted and evolves over time poses both analytical and ontological challenges. Studies that use text-mining approaches to analyse responses to major events commonly treat public discourse on social media as an undifferentiated whole, without systematically examining the extent to which that discourse consists of distinct sub-discourses or which (...) phases characterize its development. They also confound structured behavioural data with unstructured user-generated data in their sampling methods. The present study aims to demonstrate how one might go about addressing both of these sets of challenges by combining corpus linguistic methods with a data-driven text-mining approach to gain a better understanding of how the public discourse around COVID-19 developed over time and what topics combine to form this discourse in the Australian Twittersphere over a period of nearly four months. By combining text mining and corpus linguistics, this study exemplifies how both approaches can complement each other productively. (shrink)

We examined the relationship between political affiliation, perceptual estimates, and subjective judgements of disease prevalence and mortality across three chart types. An online survey exposed separate groups of participants to charts displaying COVID-19 data or COVID-19 data labeled ‘Influenza ’. Block 1 examined responses to cross-sectional mortality data ; results revealed that perceptual estimates comparing mortality in two countries were similar across political affiliations and chart types, while subjective judgements revealed a disease x political party interaction. Although Democrats and Republicans (...) provided similar proportion estimates, Democrats interpreted mortality to be higher than Republicans; Democrats also interpreted mortality to be higher for COVID-19 than Influenza. Block 2 examined responses to time series ; Democrats and Republicans estimated greater slopes for COVID-19 trend lines than Influenza lines ; subjective judgements revealed a disease x political party interaction. Democrats and Republicans indicated similar subjective rates of change for COVID-19 trends, and Democrats indicated lower subjective rates of change for Influenza than in any other condition. Thus, while Democrats and Republicans saw the graphs similarly in terms of percentages and line slopes, their subjective interpretations diverged. While we may see graphs of infectious disease data similarly from a purely mathematical or geometric perspective, our political affiliations may moderate how we subjectively interpret the data. (shrink)