TOWARDS A TAXONOMY OF DATA JOURNALISM

Journal of Media Critiques [JMC] doi: 10.17349/jmc117309 P-ISSN: 2056-9785 E-ISSN: 2056 9793 http://www.mediacritiques.net jmc@mediacritiques.net TOWARDS A TAXONOMY OF DATA JOURNALISM ANDREAS VEGLIS* CHARALAMPOS BRATSAS** ABSTRACT In recent years data journalism has drawn significant attention not only in academic literature but also in the media sector. Data Journalism is a new form of journalism that has gradually appeared over the last decade, driven by the availability of data in digital form. Currently a significant amount of data journalism projects are being produced all over the world. These projects vary considerably in terms of structure and visualization characteristics. As a result of the above it would be interesting to propose a taxonomy of data journalism projects that can help future data journalists to choose the appropriate type of projects that will be suitable for their needs. This classification could be based on certain characteristics of the data journalism projects. The proposed taxonomy will take into account various parameters that play an important role in data journalist projects and especially in the type and the role of the visualization. Keywords: Data Journalism, Taxonomy, Visualizations, Stages, Interactivity. INTRODUCTION The introduction of Information and Communication Technologies (ICTs) has transformed journalism by the digitalization of the work process as well as the introduction of the internet along with its services (Veglis 2009). New types of journalism have emerged, namely multimedia journalism (Bull 2010), and data journalism (Gray/Chambers/Bounegru 2012; Felle/Mair/Radcliffe 2015), which require journalists to have special ICT skills. In the recent years data journalism has drawn significant attention both in the academic literature but also in the area of new developments in digital news production (Appelgrena/Nygren 2014; Bradshaw 2011a; Bradshaw 2011b; Fink/Anderson 2015; Mair/Keeble 2014). Data journalism is widely considered to be the future of journalism (Knight 2015). It is a new form of journalism that gradually appeared through the last decade, driven by the availability of data in digital form. In today’s digital world almost everything can be described with numbers (Gray/Chambers/Bounegru 2012). Data journalism is a journalism specialty reflecting the increased role that numerical data has in the production and distribution of information in the digital era (Thibodeaux 2011). * Professor of Media Technology, School of Journalism & MC, Aristotle University of Thessaloniki, OKF-Greece. veglis@jour.auth.gr ** School of Mathematics, Aristotle University of Thessaloniki, OKF-Greece. cbratsas@math.auth.gr 110 Towards A Taxonomy of Data Journalism Data can be the source of data journalism, or/and it can be the tool with which the story is told (Gray/Chambers/Bounegru, 2012). Deuze argues that interactivity is the added value of online journalism, since it adds characteristics that do not exist in traditional journalism (Deuze 2001). Data journalism when conducted online inherits all online journalism’s characteristics, plus the possibility of interactive visualizations that opens new possibilities for interaction between users and content. Today there are a significant number of data journalism projects which are being produced all over the world. These projects vary considerably both in terms of structure, and visualization characteristics. Thus, it will be interesting to create a classification of data journalism projects that can help future data journalists to choose the appropriate type of projects that are suitable for their subject. This paper will attempt to propose a detail classification that will take into account the various parameters that play an important role in data journalism projects and especially in the part of visualization. The first obvious parameter should be the existence or absence of interactivity. Certainly, static visualizations are more suitable for print but they are also used on the web. A second parameter that must be included is the type of interactivity a data journalism project may utilize. Many scholars in the last 15 years have studied interactivity especially in media web sites (Spyridou/Veglis 2008). Another parameter that must be examined is the amount of text that is included in the project as well as its role. Specifically, we can have a case where the visualization supplements the text as well as one where the visualization in the centre of the project and the text plays a supplemental role; explaining parts of the visualization. The rest of the paper is organized as follows: a brief discussion on the evolution of data journalism in presented in section two. Sections three and four include the definition of data journalism as well as a discussion on the data journalism stages. The interactivity in journalism is briefly presented in section five. The main section of the paper (types of data journalism) presents existing taxonomies which have been introduced by data journalists and discusses the proposed taxonomy of data journalism projects. This will be followed by the proposed taxonomy that has been tested by classifying data journalism articles from the Guardian. The last section of the paper summarizes the findings and briefly discusses future extensions of this work. EVOLUTION OF DATA JOURNALISM The term data journalism started to attract attention at the end of the previous century. Examples of data journalism appeared quite early. According to Simon Rogers the first example of data journalism was published in the Guardian in 1821. It concerned the number of students who attended school and the costs per school in Manchester (Gray/Chambers/Bounegru 2012). The concept of data journalism is not new, it has been around since the beginning of the digitalization and digital data has been utilized in news production since the late 60s in US newspapers (Parasied/Dagiral 2012). At the end of the 20th century, employing a great deal of/large amounts of data to write an article was difficult and required skills that went beyond the capabilities of the average journalist. Some news organizations in the United States and Great Britain even hired programmers that worked on novel news products (Parasied/Dagiral 2012). Journalists used to rely on information provided by various sources (governments, officials, research studies, etc.). Of course, there were some cases of investigative Journal of Media Critiques [JMC] – Vol.3 No.11 2017 111 journalism where journalists were able to find resources to gather and analyse their own data and publish their results in articles. However the situation changed rapidly as a growing amount of data gradually became available online and efficient tools, with which anyone could analyse, visualize and publish large amounts of data, appeared (Bradshaw/Rohumaa 2011; Sirkkunen 2011). Data journalism gradually emerged with the rapid introduction of ICTs and the availability of data in digital form. The term data journalism is synonymous with datadriven journalism while the older term, computer-assisted reporting has vanished since it was introduced at the early stages of computer history (Bradshaw, 2010). It is worth noting that in the case of data journalism there is an increased interaction between journalists and people in several other fields such as design, computer science and statistics (Thibodeaux 2011; Veglis/Bratsas 2017). DEFINITION OF DATA JOURNALISM The term data journalism is attributed to Simon Rogers who first mentioned it in a post to the Guardian Insider Blog (Knight 2015). It can be viewed as a process that begins with analysing and continues by filtering and visualizing data in a form that links to a narrative (Lorenz 2010). It combines spreadsheets, graphics data analysis and the biggest news stories (Rogers 2008). It is fundamentally the production of news graphics and includes elements of design and interactivity (Bradshaw 2010; Lorenz 2010; Rogers 2008). Megan Knight (2015) describes data journalism as “a story whose primary source or “peg” is numeric (rather than anecdotal), or a story which contains a substantial element of data or visualization”. Veglis and Bratsas (2017) proposed a definition in order to better address the power of visualization and interactivity that are significant factors in data journalism. They defined data journalism as the process of extracting useful information from data, writing articles based on the information and embedding visualizations (interacting in some cases) in the articles that help readers to understand the significant of the story or allow them to pinpoint data that relate to them. DATA JOURNALISM STAGES Veglis and Bratsas (2017) organized the data journalism workflow in six stages, entitled: Data Compilation, Data Cleaning, Data Understanding, Data Validation, Data Visualization and Article Writing. The stages are depicted in figure 1. Data Compilation Data Cleaning Data Understanding Data Validation Data Visualization Article Writing ? Figure 1: Data Journalism stages (Veglis and Bratsas, 2017) Data compilation: a data journalism project begins in one of two ways: either the journalist has a question that needs data or a dataset that needs questioning. The compilation of data can take one of the following forms: (i) data may be supplied directly by an organization (in some cases in the form of open data), (ii) data may be found with 112 Towards A Taxonomy of Data Journalism the help of advanced searching techniques, (iii) data may be compiled by scraping web pages, (iv) data may be collected by converting documents to other formats that can be analyzed, and (v) data may be collected by means of observation, surveys, online forms or crowdsourcing (Bradshaw 2011a). Data Cleaning: also known as data scrubbing is the process of detecting and correcting corrupted or incorrect records from a dataset (Wu 2013). This can be accomplished by removing human errors and converting the data into a format that is consistent with other data the journalist is using (Bradshaw 2011b). Data Understanding: datasets usually include various codes that represent categories, classifications or locations, and special terminology that it is not understood by journalists. Frequently further data is required in order for existing data to become meaningful. Overall journalists must be data-literate, meaning that they must be able to consume knowledge, produce coherently and think critically about data (Veglis/ Bratsas 2017). Data Validation: this stage includes the process of cross-checking the original data and obtaining further data from sources in order to enrich the available information (Silverman 2014; Veglis 2013). It is necessary to say that like any source, datasets cannot always be trusted since they come with their own histories, biases, and objectives. That means that journalists have to investigate issues like: who gathered it, when, for what purpose and how it was gathered (Bradshaw 2011a). This can be accomplished by investigating the history of the creation of the dataset, by finding references to the dataset or by using other sources of information that refer to the same subject being investigated (Silverman 2014; Veglis/Bratsas 2017). Data Visualization: is the graphical display of abstract information for data analysis and communication purposes (Cairo 2013). The information and more specifically statistical information is abstract since it describes things that are not physical. The transformation of the abstract into physical representation can only succeed if we understand a little about visual perception and cognition. In other words, in order to visualize data effectively, one must follow design principles that are derived from an understanding of human perception (Card/Mackinlay/Shneiderman 1999; Few 2013). Static data visualizations offer only pre-composed ‘views’ of data. Interactive data visualization supports multiple static views in order to present a variety of perspectives on the same information. Important stories include ‘hidden’ data and interactive data visualization is the appropriate mean to discover, understand and present these stories. In interactive data visualization, there is a user input (a control of some aspect of the visual representation of information) and the changes made by the user must be incorporated into the visualization in a timely manner (Veglis 2015; Veglis/Bratsas 2017). Article Writing: is the last stage in a data journalism project and depending on the intended publication medium, the article may include special characteristics (for example external links other articles or related material, multimedia content, mash-ups, static or interactive visualizations) in order to fully exploit the medium’s potentials (Veglis/Bratsas 2017). The amount of text that is included in the data journalism article along with the visualizations may vary considerably. Specifically, we can have the case where the visualization supplements the text (which is quite extended) as well as the case where the visualization is the centre of the project and the text plays a supplemental role, explaining parts of the visualization. Journal of Media Critiques [JMC] – Vol.3 No.11 2017 113 INTERACTIVITY IN JOURNALISM Since the introduction of the WWW in the journalism practices, the concept of interactivity has attracted the attention of media scholars (Bucy 2004; Jensen 1998; Spyridou/Veglis 2008). Many definitions of interactivity were introduced (Spyridou/Veglis 2008), but the definition that best suits the case of interactive visualizations in media projects is: “a measure of a medium’s potential ability to let the user exert an amount of influence on the content and/or form of the mediated communication” (Jensen 1998). According to Bucy (2004) there are two types of interactivity (i) content (or user–to– system) interactivity (involves the control that news consumers exercise over the selection and presentation of editorial content), and (ii) interpersonal (or user–to–user) interactivity (involves person–to–person conversations mediated by a network). Jensen (1998) categorizes interactivity into four types: Transmissional interactivity: it is a measure of a media’s potential ability to let the user choose from a continuous stream of information in a one–way communication. Consultational interactivity: in this case there is a measure of a media’s potential ability to let the user choose from an existing selection of pre–generated information in a two–way media communication. Conversational interactivity: it is a measure of a media’s potential ability to let the user produce and input his/her own information in a two–way communication, which his stored or displayed in real time. Registrational interactivity: in this case there is a measure of a media’s potential ability to register information from and thereby also adapt and/or respond to a given user’s needs and actions. In the above-presented typology of types of interactivity, we can distinguish those types that can be applied in the case of interactive visualizations. The transmissional interactivity can apply to the majority of the visualizations. Also, the consultational interactivity can cover all the visualization projects that offer many views of the same data. Lastly, the registrational interactivity can be applied to the interactive visualizations that allow the input of user data that can produce an altered visualization in real time. TYPES OF DATA JOURNALISM PROJECTS Megan Knight (2015) proposed a ranking of the data element types based on the level of interpretation and analysis required to produce them. This ranking included: number pullquote, static map, list and timelines, table, graphs and charts, dynamic map, textual analysis, and info graphics. Simon Rogers (Bradshaw/Cairo/Doig/Rogers/KayserBril 2015), a journalist and data editor, proposed five types of data journalism projects. This classification is based on the type of data they include and the methods they employ. Specifically: By just the facts: usually includes publicly available data and produces a single visualization that supports the issue of the project. Data-based news stories: refers to issues that are in the public eye and reveals numbers behind the news typical examples are data journalism projects on election or voting results. Local data telling stories: involves the production of data journalism projects on subjects that interest the local community from local news organizations with small resources. Data is usually provided by governments. 114 Towards A Taxonomy of Data Journalism Analysis and background: this kind of data journalism project attempts to explain the facts behind the news by contacting a thorough analysis and exposing great exclusives. Such projects may take many months to complete and they combine public data and reporter analysis. Deep dive investigations: in this case we have extensive investigations on big datasets and the construction of databases which can usually provide more than one news story. Typical examples are projects that were conducted by well-known news organizations around Wikileaks data dumps. Martha Kang (2015) proposed seven different types of stories, namely: Narrate change over time: the data is used in order to visualize the changes and then they explain the forces at work. Start big and drill down: in this case, the data can guide the reader from a wide view to a focused view. The reader can zoom in and out from areas (for example on a map) and access data for each specific area. Readers can also be provided with interactive filters in order to access certain data. Start small and zoom out: in this type of journalism story, the reader initially focuses on a particular part of the available information (for example in a specific country or in a specific city in a country) and then he can expand his view in a larger view and get information with a wider perspective. Highlight contrasts: in this case the narrative of the data journalism story is based on outlining the differences. Explore the intersection: this kind of data journalism story attempts to explain the case of two divergent lines of data that intersect and one overtakes the other. Dissect the factors: in this case, journalists investigate the relationship between different factors of a story. Profile the outliers: in this case, the story is focusing on outliers and attempts to explain why this is happening. Usually finding the outliers involves some data exploration. It is worth noting that the above types were derived from working with the same set of data and thus these data types should be considered as a start (Kang 2015). Martin Rosenbaum (Gray/Chambers/Bounegru 2012) based on his experience in BBC has drawn up a list of different types of data stories. The first type is measurement, which can be considered the simplest story that includes counting or totalling some numbers. The problem is that such a data story does not provide any context. The later can be accomplished with the other types of data stories, namely, proportion, internal comparison, external comparison, change over time, league tables, analysis by categories, and association. All the previously presented taxonomies are included in table I. It is worth noting that the proposed taxonomies are not based on the same principles. The taxonomies of Kang and Rosenbaum have some common features and they are based on the method of presenting data in a context. On the other hand, the data journalism types proposed by Rogers are based on various parameters (kind of data they include, the method they incorporate, the potential audience of the story, etc). Journal of Media Critiques [JMC] – Vol.3 No.11 2017 115 Table 1: Types of data journalism projects/stories proposed by others. Simon Rogers Martha Kang Martin Rosenbaum by just the facts narrate change over time Measurement data-based news start big and drill down Proportion stories local data telling start small and zoom out internal comparison stories analysis and highlight contrasts external comparison background deep dive explore the intersection change over time investigations dissect the factors league tables profile the outliers analysis by categories Association In the majority of the cases of data journalism stories the visualizations supplement the narrative which is conveyed through the text. However, it is worth noting that in some cases the visualization is constructed in such a manner so as to include the narrative of the news story. Thus, the visualization becomes the centre of the data journalism story and the text (which is usually quite limited) supplements or explains the visualization. Alberto Cairo refers to it as structuring the info-graphics as a story (Bradshaw/Cairo/Doig/Rogers/Kayser-Bril 2015). Based on the previous discussion a novel data journalism project taxonomy will be presented. It is obvious that data journalism projects have many features and thus many taxonomies may be produced that take into account different characteristics. The visualization in a news story is an important characteristic but it cannot be used exclusively in order to establish data journalism taxonomy. The taxonomies proposed by Kang and Rosenbaum fall in this category (Gray/Chambers/Bounegru 2012; Kang 2015). Simon Rogers (2014) approaches the issue from a different angle. He treats data journalism projects as a total and classifies them based on specific characteristics namely type of data, method of gathering data and intended audience. The problem with this approach is that it totally excludes the visualization parameter and especially the case of interactive visualization that offers enormous capabilities in exploring the available data. So the proposed approach is not to suggest a taxonomy that will substitute the existing Rogers’ taxonomy. The proposed taxonomy will not take into account the method of investigation or the intended audience or the method of data gathering but will focus exclusively on the method of presenting the information to the audience. 116 Towards A Taxonomy of Data Journalism Figure 2: The proposed taxonomy. The proposed taxonomy is depicted in figure 2. The taxonomy has a hierarchical structure. Initially there is a distinction between projects that include only numbers, projects that include tables and projects that include some kind of visualization. Projects that employ visualizations can be categorized between projects where the visualization is part of the story (and supplements or adds value to the narrative) and projects where the visualizations are structured as a story (the visualizations are the main part of the story and the actual text of the project is quite limited and attempts to explain and clarify parts of the visualization). Both of the previous mentioned categories can include static or interactive visualizations. Projects with static visualizations can be published in both static (for example print) and also interactive mediums (for example the WWW). Finally projects with interactive visualizations can be further categorized depending on the type of interactivity they utilize: (i) transmissional that includes projects that utilize simple interactive visualizations that allow the user to view the visualization and provide him with some additional explanation of various elements of the visualization in Journal of Media Critiques [JMC] – Vol.3 No.11 2017 117 the form of pop up information, (ii) consultational that includes projects that offer multiple views of the same data, as well as projects that include interactive visualization that allow the user to zoom in certain areas (maps, timelines, etc), and (iii) conversational that includes projects that visualization that accept input data from the user which can alter the visualization. It is worth noting that consultational and conversational interactive visualization projects support the customization of the user’s experience to its needs. In addition to the above characteristics that were employed in the proposed taxonomy, there are some other features that are considered to be important and can be applied in several of the previously described categories, thus, it is worth presenting them. The annotation layer is a feature that highlights interesting issues in the data which can be explored by the reader. This layer supplements the visualization that usually consists of a visual representation of the data and a navigation layer which can be used by readers in order to explore the data (Bradshaw/Cairo/Doig/Rogers/ KayserBril 2015). It is important to mention that the annotation layer can be employed both in static and interactive visualizations, each visualization included in a project usually incorporates a proper headline, a short introduction that explains the context of the data for the graphic. This introduction to the graphic can help the reader understand the data more easily. SURVEY OF DATA JOURNALISM ARTICLES In order to test the proposed taxonomy, a survey was conducted regarding the data journalism articles which have been published by one of the leading media organization in the world as far as data journalism in concerned, The Guardian (http://www.guardian.co.uk). The survey was conducted from March till October of 2016, and 62 data journalism articles were found and classified based on the proposed taxonomy. 90.32% of the articles in the sample included some kind of visualization, 8.07 included only numbers and 1.61% only tables. It is worth noting that many articles with visualizations also included tables. The majority of the visualizations (78.6%) that were included in the data journalism articles were static and only 21.4 were interactive. 41.2% of the visualizations were found to be part of the story and 58.8% were structured as a story. Finally as far as the type of interactivity is concerned 25% were found to be transmissional 37.5%, consultational and 37.5% conversational. Overall the survey indicated that the proposed taxonomy can be employed in order to classify data journalism projects and no discrepancies in the taxonomy were identified. Based on the results of the survey we can conclude that the majority of the data journalism projects tend to include some kind of visualization and three out of four visualizations are static. Also there seems to be a tendency to structure the visualization as a story rather than it being part of the story. Lastly when interactive visualizations are employed there is no clear preference to a certain type of interactivity, but the number of article which included interactive visualizations was quite limited. CONCLUSIONS This paper discusses the issue of taxonomies of data journalism projects. A detail discussion concerning data journalism was conducted that offers valuable knowledge about a potential taxonomy. Special attention was given to interactive visualization and to journalism interactivity. The proposed taxonomy addresses issues that are related to 118 Towards A Taxonomy of Data Journalism the characteristics of data journalism. An important role in the proposed types of data journalism is played by the existence of visualizations and the interaction they may offer to the potential user. The proposed taxonomy was put into test by classifying data journalism articles published in the Guardian. Future extension of this work should include an analysis of the types of data journalism projects which are produced from major news organizations around the world, and also an attempt to correlate the subject of the articles with the types of the proposed taxonomy. Without doubt, data journalism is a very promising type of journalism with many possibilities that have not been exploited yet. As society gradually moves to the era of big data, the value of data journalism will increase exponentially. Journalists need to see this development as an opportunity to seek and acquire the necessary skills in order to stay competitive (Veglis/Pomportsis 2014). Journal of Media Critiques [JMC] – Vol.3 No.11 2017 119 REFERENCES Appelgrena, Ester; Nygren Gunnar (2014): Data Journalism in Sweden Introducing new methods and genres of journalism into “old” organizations In: Digital Journalism, 2:3, pp. 394-405. Bradshaw, Paul (2011a): The inverted pyramid of data journalism In: Online Journalism Blog [http://onlinejournalismblog.com/2011/07/07/the-inverted-pyramidof-data-journalism]. Bradshaw, Paul (2011b): Data journalism survey: a mixed picture In: Data Driven Journalism. [http://datadrivenjournalism.net/news_and_analysis/data_ journalism_ survey_analysis]. Bradshaw, Paul (2010): How to be a data journalist. The Guardian Data Journalism [http://www.Guardian.co.uk/news/datablog/2010/oct/01/data-journalism-how-toguide]. Bradshaw, Paul; Rohumaa, Liisa (2011): The Online Journalism Handbook: Skills to survive and thrive in the digital age. Longman Publishing Group. Bradshaw Paul; Cairo, Alberto; Doig, Steve; Rogers, Simon; Kayser-Bril, Nickolas (2015): Data Driven Journalism - Doing Journalism with Data: First Steps, Skills and Tools. LEARNO.NET Professional Video Training by the European Journalism Centre [http://learno.net/courses/doing-journalism-with-data-first-steps-skills-and-tools]. Bucy, Erik (2004): Second generation Net news: Interactivity and information accessibility in the online environment. International Journal on Media Management, volume 6, numbers 1–2, pp. 102–113. Bull, Andy (2010): Multimedia Journalism: A Practical Guide. London and New York: Routledge. Cairo, Alberto (2013): The functional art: an introduction to information graphics and visualization. New Riders. Card, Stuart; Mackinlay, Jock; Shneiderman, Ben (1999): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann. Deuze, Mark (2001): Online journalism: Modelling the first generation of news media on the World Wide Web. First Monday, volume 6, number 10 [http://firstmonday.org/ojs/index.php/fm/article/view/893]. Gray, Jonathan; Chambers, Lucy; Bounegru, Liliana (2012): The Data Journalism Handbook [http://datajournalismhandbook.org/]. 120 Towards A Taxonomy of Data Journalism Felle Tom; Mair, John; Radcliffe, Damian (2015): Data Journalism: Inside the global future. Abramis Few, Stephen (2013). Data Visualization for Human Perception. In: Soegaard, Mads and Dam, Rikke Friis (eds.). The Encyclopedia of Human-Computer Interaction, 2nd Ed. Aarhus, Denmark: The Interaction Design Foundation (2013) [http://www.interaction-design.org/encyclopedia/data_visualization_for_human_ perception.html]. Fink, Katherine; Anderson, Chris (2015): Data Journalism in the United State, Journalism Studies, Vol 16, issue 4, pp. 467-481. Jensen, Jensen (1998): Interactivity – Tracking a new concept in media and communication studies. Nordicom Review, volume 19, number 1, pp. 185–204. Kang, Martha (2015): Exploring the 7 Different Types of Data Stories. Mediashift [http://mediashift.org/2015/06/exploring-the-7-different-types-of-data-stories/]. Knight, Megan (2015): Data Journalism in the UK: a preliminary analysis of form and content. Journal of Media Practice, 16:1, pp. 55-72. Lorenz, Mirko (2010): Data driven journalism: What is there to learn? Presented at IJ-7 Innovation Journalism Conference, 7–9 June, Stanford, CA. Mair, John; Keeble, Richard, Lance (2014): Data Journalism: Mapping the future, Abramis Academic Publishing. Parasie, Sylvain; Dagiral, Eric (2013): ‘Data-driven journalism and the public good: “Computer-assisted-reporters” and “programmer-journalists” in Chicago’. New Media & Society, 15:6, pp. 853–871. Rogers, Simon (2014): Introduction to data journalism. Simon Rogers-Data Journalism and other Curiosities [http://simonrogers.net/2014/05/25/introduction-todata-journalism/]. Rogers, Simon (2008): Turning official figures into understandable graphics, at the press of a button. Inside the Guardian Blog, [http://www.Guardian.co.uk/ help/insideGuardian/2008/dec/18/unemploymentdata]. Silverman, Craig (2014): Verification Handbook. European Journalism Centre [http://verificationhandbook.com/downloads/verification.handbook.pdf]. Sirkkunen, Esa (2011): Trends in Data Journalism, WP 3, Hyperlocal, [http://virtual.vtt.fi/virtual/nextmedia/Deliverables-2011/D3.2.1.2.B_ Hyperlocal_Trends_In%20Data_Journalism.pdf]. Journal of Media Critiques [JMC] – Vol.3 No.11 2017 121 Spyridou, Paschalia; Veglis, Andreas (2008): Exploring structural interactivity in online newspapers: A look at the Greek Web landscape. First Monday, Vol 13 Number 5 [http://firstmonday.org/ojs/index.php/fm/article/view/2164/1960]. Thibodeaux, Troy (2011): 5 tips for getting started in data journalism, Poynter: A Global Leader in Journalism [http://www.poynter.org/news/mediainnovation/147734/5-tips-for-getting-started-in-data-journalism]. Veglis, Andreas (2015): Teaching Data Journalism in the School of Journalism & MC-Greece. Paper presented at Ejta2015, Narrative Journalism and Storytelling on Digital Platforms (Teacher Conference), Milan, Italy. Veglis, Andreas (2013): Education of Journalists on ICTs: Issues and Opportunities. Journal of Applied Journalism & Media Studies, 2(2), 265-279. Veglis, Andreas (2009): Cross Media Communication in newspaper organizations. Ιn: Poulymerakou, A. Pouloudi, N. and Pramatari, K. (eds), 4th Mediterranean Conference on Information Systems, Athens Greece. Veglis, Andreas; Bratsas, Charalampos (2017): Journalists in the age of Data Journalism: The case of Greece. Journal of Applied Journalism & Media Studies, Vol 6, Issue 2 (forthcoming). Veglis, Andreas; Pomportsis, Andreas (2014): Journalists in the age of ICTs: Work demands and Educational needs. Journalism & Mass Communication Educator, Volume 69. Issue 1 pp. 61 – 75. Wu, Shaomin (2013): A review on coarse warranty data and analysis. Reliability Engineering and System, 114, pp 1-11.