Data visualization in Indian print media: a comparative study of English and Hindi newspapers

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Indira Gandhi National Open University

Amity University

Data visualization in Indian print media: a comparative study of English and Hindi newspapers

Amit Kumar, Poonam Gaur

Noida

Abstract

The advancing technology is affecting every aspect of life and journalism is also not untouched by this. Due to digitalization, huge amount of data is being generated and the continuous advancement of computer science has made it possible to extract meaningful information by storing and analysing this huge data. The term “data journalism” has become quite popular over the last decade. Analysing data sets, extracting newsworthy information from it and passing it on to the public is data journalism. Data visualization also has a very important place in this whole process. Data visualization is used to communicate information extracted from the data to the users in a clear, interesting and engaging way. The amount of data-based content has started increasing in the news media as well, so the importance of data visualization has also increased. The use of data visualization improves readers' reading experience and also helps to better understand the data-based content. This preliminary study focuses on the use of data visualizations by English and Hindi newspapers in India. In this research, a comparative study of various aspects of the use of data visualizations in English and Hindi newspapers has been done. Content analysis with quantitative approach has been employed as the research method. This study reveals that there is a big difference in every aspect of the use of data visualizations in English and Hindi newspapers. English newspaper used data visualizations in a better way than their Hindi counterpart.

Keywords: data visualization, data journalism, print media, English and Hindi newspapers

Аннотация

Визуализация данных в индийских печатных СМИ: сравнительное исследование газет на английском языке и хинди

А. Кумар¹, П. Гаур²

Национальный открытый университет имени Индиры Ганди

Передовые технологии влияют на все аспекты жизни, журналистика не остается в стороне от этих процессов. Благодаря цифровизации генерируется огромное количество данных, при этом значимую информацию теперь можно хранить, получать и анализировать. Термин «журналистика данных» стал популярным за последнее десятилетие. Анализ наборов данных, извлечение заслуживающей внимания информации и передача ее общественности - это и есть журналистика данных. Визуализация данных также занимает очень важное место в этом процессе. Визуализация используется для того, чтобы передать информацию, извлеченную из баз данных, наглядным и интересным способом. Объем контента на основе данных начал расти в СМИ, поэтому важность визуализации данных также возросла. Использование визуализации данных обогащает пользовательский опыт и помогает лучше понять контент на основе данных. Настоящее пилотное исследование посвящено визуализации данных в газетах Индии, издающихся на двух государственных языках - английском и хинди. Сравнительное изучение различных аспектов использования визуализации данных было проведено с помощью контент-анализа. В результате выявлен существенный разрыв в уровне использования визуализации данных в газетах: СМИ на английском используют визуализацию данных значительно чаще и эффективнее, чем СМИ на хинди.

Ключевые слова: визуализация данных, журналистика данных, печатные СМИ, газеты на английском языке, газеты на хинди

Introduction

Digitalization, or computerization, is increasing rapidly in all areas of human life and due to this huge amount of data is being generated. The continuous advancement of computer science has made it possible to collect and analyse this huge data and extract useful information from it. In this scenario, the importance of data visualization is also growing rapidly. The main idea of data visualization is to present the data visually in such a way that the human eye can derive meaning from their structure and patterns [1].

Like other fields, journalism also used advanced techniques of data storage, analysis and visualization. As a result, data journalism came into existence. Modern techniques of data analysis enabled the analysis of documents containing thousands and millions of pages and revelation of interrelations among them. As a result, the public was exposed to many big news. Most media organizations are understanding the usefulness of data journalism and are also giving necessary training to their staff [2].

Use of data in journalism is not new but modern technologies have made it possible to extract newsworthy information by holistically analysing large data sets. New technologies have also made data visualization relatively more effective and easier. WikiLeaks's secret data sets are considered to be the beginning of current data journalism, but it is also true that the data journalism is not limited only to the investigative news. Uskali and Kuutti (2015) classified data journalism into two main categories: investigative data journalism and general data journalism. General data journalism is also called as daily data journalism. According to them, investigative data journalism needs a lot of time. It may be few months or in some cases years. It requires advanced level data skills. Unofficial, confidential and data leaks have great significance in this type of data journalism. On the other hand, in general data journalism, journalists have only few hours or days to complete the data stories. Here, basic level data skills are required and public and open access data sets are used. They also talked about one more type of data journalism, realtime data journalism. This type of data journalism is based on the automatic creation of news stories and mainly practised in sports or financial news. At present it is in nascent stage [3].

Although this study is mainly focused on data visualization, the discussion on data journalism is also very important because data visualization is a part of data journalism. Gray, Chambers and Bounegru (2012) explained the process of data journalism and divided it into three main parts - gathering data, filtering data, and visualizing data. According to them, data visualization is an integral part of data journalism and must be used in data-driven storytelling [2]. A data journalist can successfully present a complex data-based story with the help of engaging infographics.

After discussing data journalism and data visualization, it is required now to shift the direction of the discourse towards language related aspects. Hindi and English together occupy more than half of Indian print media in terms of circulation and number of publication as well. According to the latest report by Registrar of Newspapers for India (RNI) (2019), about 41% of the total registered publications are in Hindi and 12% in English. Following the same trend, Hindi has the highest share (45%) in total circulation followed by English (12%). It means that Hindi and English together have 53% share of Indian print media in terms of number of publications. As far as circulation is concerned, these two languages have 57% share of overall circulation of Indian print media [4].

This study is aimed to compare the newspapers of two different languages, English and Hindi. In India, Hindi and English do not differ only due to linguistic features but the socioeconomic dimensions are also different. Readers of English newspapers and readers of Hindi newspapers have different socioeconomic status. So, while comparing the data visualization of Hindi and English newspapers, a brief discussion on the socioeconomic dimensions of this issue is quite relevant.

India has a complex language scenario. Total 22 Indian languages including Hindi have been included in the eighth schedule of the Indian Constitution [5]. Many other languages are also demanding for inclusion in the eighth schedule. However, article 343 (1) of the Indian Constitution declared Hindi as the official language of the union. According to Kumar (2014), the public sphere of India splits into two major divisions: English and Vernacular. Some people describe it as the division of India and Bharat. The distribution of twelve subcategories of social- economic classification (SEC) system, generally used by market researchers, shows that the higher categories consist of English speaking public [6].

English is considered as an important tool for academic, economic and social upliftment in India. Even the politicians who raise the sentiments of the pride of mother language send their own children to English medium schools [7]. Now, it is quite clear that English enjoys a superior position in Indian society. This language is considered as a language of higher education, better employment and ensures upper social status. Though Hindi is the most widely spoken language in India, as far as socioeconomic dimensions are concerned, English is enjoying higher status. This situation may also impact the content of newspapers including data visualization of these two languages.

Review of literature

In the field of mass communication, researchers of data journalism have often made data visualization an important part of their studies. Some researchers have also done independent research on data visualization, but their number is relatively less. Highlighting the importance of data journalism Arthur (2010) quoted Tim Berner Lee, the inventor of the World Wide Web and said that news stories of the future would not come from chatting in the bar, but from poring over rows of data. Lee declared that data-driven journalism is the future [8]. Following the same line Glover and Beard (2017) find that data journalism is becoming popular among journalists and media organizations. Data-based reporting has now taken root in media and a large number of journalists are equipping themselves with the skills of data journalism. According to journalists, data-based journalism puts the truth in more credible ways [9].

A number of scholars defined data visualization. According to Kirk (2016), the visual representation of data to facilitate understanding is data visualization [10]. Weber, Engebretsen and Kennedy (2018) also define data visualization as a visual representation of data generated to strengthen the cognitive processing and the social application of the data represented. They considered graphs, charts, maps and timelines, or a combination of these as classic data visualizations [11].

As mentioned above, data visualization is an integral part of data journalism. A number of scholars also expressed the same. According to Uskali and Kuutti (2015), data visualization is an essential part of data journalism and every data journalist should be equipped with at least basic skills of data visualization [3]. Lorenz also found data visualization as one of the three main dimensions of the data journalism [12]. According to Stalph (2018), data visualization is the fundamental feature of data-driven stories. General data-based stories or daily data-driven news can differentiate themselves from traditional news stories by focusing on data sources as main sources and data visualizations [13].

A number of studies have been conducted on the use of different types of data visualizations in the field of journalism. Stalph (2018) found bar charts as the most popular data visualization followed by maps and line chart [13]. According to Loosen, Reimer and De Silva-Schmidt (2017), charts and maps are the most popular data visualizations because they are quite easy to create and they can be made with free software as well. According to them, availability of time to the journalists also decides the type of visualization [14]. While doing content analysis of data journalism award finalists (2012 to 2015), Young, Hermida and Fulda (2018) also found maps and graphs to be the most commonly used data visualizations [15].

Objectives of the study

The objectives of this study were following:

- to compare the use of data visualizations for presenting news in select English and Hindi newspapers;

- to find out the different types of data visualizations used in select English and Hindi newspapers;

- to examine and compare data sources of data visualizations used in select English and Hindi newspapers;

- to analyse the importance given to data visualizations in select English and Hindi newspapers.

Research methodology

Content analysis with quantitative approach was employed to achieve the objectives of this study. Content analysis has been very popular research method in communication research. According to Krippendorff (1989), mass media have been the most important area for the content analysis and the available literature is full of the content analysis studies of newspapers, books, magazines, films, television programming and radio broadcasts [16]. A number of definitions are available for content analysis. A big section of scholars defines content analysis as a quantitative method. According to Berelson (1952), content analysis “is a research technique for the objective, systematic, and quantitative description of the manifest content of communication” [17. P. 18]. Therefore, in this study, content analysis was applied for quantitative description of different aspects of data-driven news content in Indian English and Hindi newspapers.

Sampling. To take the suitable sample for this content analysis, multi-stage sampling approach was applied. The whole Indian English and Hindi newspapers were the population of this study, therefore sampling was required and it was done at the two stages - first, selection of Indian English and Hindi newspapers and second, selection of dates. For selection of newspapers, purposive sampling was employed. According to Wimmer and Dominick (2011), purposive sampling is a nonprobability sampling that chooses units of population with certain features or qualities and filter out those who do not meet these criteria [18]. Following the same, circulation was fixed as the criterion for selection of newspapers and the English and Hindi newspapers with highest circulation were selected as samples for this study. According to the latest report of RNI (2019), the largest circulated multi-edition daily was “Dainik Bhaskar”, a Hindi newspaper, and the second largest circulated multi-edition daily was “The Times of India”, an English daily [4]. So, New Delhi editions of “The Times of India” and “Dainik Bhaskar” were selected as sample for this study.

As far as selection of dates was concerned, constructed week sampling was applied. According to Rife, Aust and Lacy (1993) constructed week sampling is more suitable than purely random and consecutive day sampling for the content analysis of newspapers where content of weekdays and weekend may differ or content planning may be different for different days. So, representation of all seven days is required [19]. Hester and Dougall (2007) also advocated for constructed week sampling for better results in news content analysis. According to them, constructed week sampling should be used to control the bias of cyclic trends in news coverage [20]. Hence, constructed week sampling was employed in this study too.

One constructed week was taken from two months, January and February 2020. This constructed week was created by selecting Monday from the first week, Tuesday from the second week, Wednesday from the third week, Thursday from the fourth week, Friday from the fifth week, Saturday from the sixth week and Sunday from the seventh week. It had representation of all seven days.

Findings and analysis

In this section, key findings of this study will be discussed. These findings will present a comparative picture of English and Hindi newspapers in the context of data visualization.

Use of data visualization. Findings revealed that the English newspaper (“The Times of India”) used data visualization significantly more than the Hindi newspaper (“Dainik Bhaskar”). Figure 1 illustrates this difference clearly.

Figure 1. Use of data visualizations by English and Hindi newspapers

In the sample taken for study, total 121 data visualizations were used by both the newspapers. The English newspaper used data visualization almost three times more than its Hindi counterpart. From this it is clear that English newspapers give more importance to data based visualization than Hindi newspapers. As far as per day use of data visualization is concerned, English newspaper was also far ahead in this. English newspaper used 13 data visualizations per day {M (Mean) = 13, SD (Standard deviation) = 4.86}, on the other hand, Hindi newspaper used only 4.29 visualizations per day {M (Mean) = 4.29, SD (Standard deviation) = 1.70}.

Themes/subjects of data visualizations. Theme/subject wise analysis of data visualizations was also one of the objectives of this study. Table 1 presents the findings related to it.

Table 1 Theme/subject wise distribution of data visualizations

11. Weber, W., Engebretsen, M., & Kennedy, H. (2018). Data stories: rethinking journalistic storytelling in the context of data journalism. Studies in Communication Sciences, 2018(1), 191-206.

12. Lorenz, M. (n.d.). Why Journalists Should Use Data. Retrieved March 2, 2020, from https://datajournalism.com/read/handbook/one/introduction/why-journalists-should-use-data

13. Stalph, F. (2018). Classifying Data Journalism: A content analysis of daily data-driven stories. Journalism Practice, 12(10), 1332-1350. DOI: 10.1080/17512786.2017.1386583

14. Loosen, W., Reimer, J., & De Silva-Schmidt, F. (2017). Data-driven reporting: An ongoing (r)evolution? An analysis of projects nominated for the Data Journalism Awards 2013-2016. Journalism, 1464884917735691.

15. Young, M.L., Hermida, A., & Fulda, J. (2018). What makes for great data journalism? A content analysis of data journalism awards finalists 2012-2015. Journalism Practice, 12(1), 115-135.

16. Krippendorff, K. (1980). Content Analysis: An Introduction to Its Methodology. Newbury Park, CA, Sage.

17. Berelson, B. (1952). Content analysis in communication research. Glencoe, IL, Free Press.

18. Wimmer, R.D., & Dominick, J.R. (2011). Mass media research: An Introduction. Boston, Wadsworth Cengage Learning.

19. Riffe, D., Aust, C.F., & Lacy, S.R. (1993). The Effectiveness of Random, Consecutive Day and Constructed Week Sampling in Newspaper Content Analysis. Journalism Quaterly, 70(1), 133-139. Retrieved January 21, 2020, from http://www.aejmc.org/home/ wp-content/uploads/2012/09/Journalism-Quarterly-1993-RiffeAustLacy-133-391 .pdf

20. Hester, J.B., & Dougall, E. (2007). The efficiency of constructed week sampling for content analysis of online news. Journalism & Mass Communication Quarterly, 84(4), 811-824.

21. John, B.K. (2015, August 19). Hijacking the front page. Retrieved March 2, 2020, from http://asu.thehoot.org/opinion/hijacking-the-front-page-8872

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