Abstract
In the second wave of the novel corona virus pandemic, where misinformation and infodemic are at their peak, data-driven reporting acts as a solution to curb misinformation and infodemic worldwide. Therefore, it is important to analyse the newspaper coverage of the COVID-19 second wave by using models of data-driven reporting in newspapers based on purpose, relationship, data presentation, type, and structure. The objective of this research was to analyse the data-driven reporting of COVID-19 and test models of data-driven reporting in the field of health communication. This research study was based on a quantitative approach, using content analysis research method, and the tool of data collection was secondary. The unstructured sampling technique was taken into consideration which included two English national dailies of India for five consecutive days. Results of this study helped the researcher in analysing data-driven reporting of COVID-19 second wave and testifying these models in the field of health communication.
TopIntroduction
In context of defining the term ‘Data Journalism’ (Berret C, 2016, p. 15), defined it “as a field that encompasses a suite of practices for collecting, analyzing, visualizing and publishing data for journalistic purposes” As coronavirus is spreading at large, UN organisation like WHO referred to “infodemic” as a second disease accompanying the Covid-19 wave in 2020. But despite this situation of misinformation and infodemic, the UN firmly believes that journalism acts as a key to the dissemination of credible information ((World Trends in Freedom of Expression and Media Development, 2020, p. 2). Hence, data journalism can contribute to curbing misinformation and infodemic worldwide.
This chapter attempts to study the advancement of data journalism in the field of health communication. Presently, health journalists are expected to be unbiased and go beyond conventional standards of reporting using data journalism as a key to authenticity especially in the pandemic situation of Coronavirus. Data journalism emerged as an important practice in the year 2014 and added new scope to media studies. Researchers introduced various workflow models of data journalism out of which The Taxonomy Model of Data Journalism (2017) gave a unique dimension to data journalism projects. However, the model emphasized more on digital media platforms unlike static mediums of communication.
As an extension to the Taxonomy Model of Data Journalism, (Sabu J, 2020) suggested two models of data-driven reporting, the first model was based on purpose and relationship and the second model reflected on the presentation, type, and structure. The suggested models provided a structural approach for print journalists and proved that data journalism as a practice was not limited to the online news portal, but can be implemented successfully in newspapers which contribute to the authenticity and genuineness of the medium.
During a pandemic, newspapers act as ombudsman with added layers of accountability and expertise. Despite multiple online-fact checking software and digital guidelines, most online news outlets are dependent on audience feedback for authenticity. On the other hand, newspapers have an active network of legitimate sources, news agencies, and reporters and they can guarantee more accuracy and accountability as compared to online news portals. Hence, newspapers are considered more reliable source of information as it safeguards its readers’ from fake news and misinformation with the help of accurate data which is a crucial need to tackle the Covid-19 effectively and efficiently. Therefore, it is important to analyze the newspaper coverage of Covid 19 (Second Wave) by using models of data-driven reporting in newspapers based on purpose, relationship, data presentation, type, and structure.
This study was based on a quantitative approach, using content analysis research method proposed by (Berelson, 1952; Berelson, 1952) and the tool of data collection was secondary. The unstructured sampling technique was taken into consideration which included 2 English prime national dailies of India namely Times of India & Hindustan Times for 5 consecutive days each. The results of this study helped the researcher in analyzing data-driven reporting of the Covid 19-second wave and testifying application of these models in the field of health communication.
Key Terms in this Chapter
Computer-Assisted Reporting: It is referred to as a process of reporting using computers to gather and analyze data that is necessary to write news stories.
Data Journalism: It is the ability to analyze numbers and to manage large data sets and interpret them correctly.
Data Visualization: It is a technique of using images, maps, diagrams, and charts to communicate data effectively.
Stand-Alone Visualization: It is a classification of structure in data visualization, wherein the visualization is independent by neither being structured as a story nor published as a part of a story.
Automated Content Analysis: It is referred to as a set of techniques used to automatically analyze the media content.
Health Communication: It is the study and practice of communicating health information to influence the personal health choices of the public by improving health literacy.
Infodemic: It is referred to excessive information on an issue that is mostly unreliable, spreads rapidly, and makes it difficult to achieve a solution or to arrive at a conclusion.
Pandemic: It is an epidemic that occurred worldwide, that is crossing the international borders resulting in affecting a majority of the population.
Stand-Alone Data: These are the data sets published in a newspaper; however they are not part of any news reports, editorials, or opinion pieces.
Data-Driven Reporting: It is a journalistic reporting process that is based on analyzing, filtering, and interpreting large data sets to develop a news story.