A New Ecology of News: Data-driven Journalism - Literature review Example

A New Ecology of News: Data-driven Journalism The processes involved in journalism have been evolving with the passage of time. With advancement intechnologies, newer processes are being developed in the journalistic sphere, with certain media technologies acting as the catalyst. Importantly, these newer processes are nullifying the need for the journalists to be on the ground in the midst of happenings to report and analyze those happenings. Instead, they can be inside the confines of their room or office, where they can just analyze the data and come up with news stories. This new process is known as Data-driven journalism, which had its beginnings in 2009. Data-driven journalism, also known as Big Data, is a journalistic process that involves gathering data, filtering them, analyzing them, and then finding patterns in that data for the purpose of writing a news story. The main technology-based resources used for this process includes openly available data, which are accessed from government channels or web-based or social media-based platforms, as well as open access publishing, and even open source software. Although, the data might be openly available, it is not easy to collect the right data, let alone analyze them and find patterns in them. Journalists have to put in a lot of hard work and utilize various techniques to gather, analyze, and find optimal patterns, so it can lead to a strong news story. In that direction, blogger plus journalist Eliot Higgins gathered data primarily through Youtube and other social media videos regarding the internal conflict in Syria, analyzed it, and found patterns regarding the unauthorized use of Croatian weapons in the conflict thereby leading to its publication as a featured news story in The New York Times in 2013. So, picking this recent news story, the discussion will be about how the journalists, particularly Eliot Higgins managed to research vast amounts of information and how this data journalism is slightly from traditional journalism, although both can coexist. About Data-Driven Journalism According to multimedia journalist Mirko Lorenz data-driven journalism is defined “as a workflow, where data is the basis for analysis, visualization and – most important – storytelling” (Baack 2011, p.2). As storytelling is the key aspect, the end product is not just about visualization of the data; instead it is about contextualization and highlighting of the important aspects in that data in the form interesting, strong, and impacting news story. The crucial player who triggered the development of this journalistic process is the non-profit journalistic organization Wikileaks, which leaked secret and sensitive US government documents dealing with War in Afghanistan and Iraq in the later part of the 2000s. The data in those documents were immense but crucial. So, it initiated the journalists to delve deeper into those data, filter them, analyze them and thereby find effective news stories related to the wars. “The coverage of Wikileaks’ huge amounts of leaked data was a challenge for newspapers…they had to figure out how to get stories out of extensive and complex data sets and how to present their findings to readers” (Baack 2011, p.1). So, these data leaks by Wikileaks mainly laid the foundation for the development of new style of news reporting known as data-driven journalism or data journalism or Big Data. Apart from depending on these leaks and other government data, journalists started to form or gather their own data through research on the web. That is, with file hosting services like Youtube and social media platforms such as Facebook, Twitter, Google Plus, newspapers’ websites, and other resources publishing a huge gamut of data in the form of videos, photos, posts, articles, and many more, journalists are accessing them to create their own database. “Social media is a good example of big data in practice. Users are generating 500m tweets and 90m blog posts on Tumblr per day. Every minute, 100 hours of video is uploaded to YouTube. The social media universe is expanding at an astronomical rate” (Moran 2014). As people throughout the world are using advanced technological tools and have increased web connectivity to project and record their views, thoughts, and observation in the form of writings, photos, and videos in the above-mentioned file hosting websites and social media websites, it provides the journalists raw data to skim, analyze and find patterns. Recent News Story In line with the above process only, Britain-based journalist Eliot Higgins came up with the weapons story of the Syrian conflict, which was published as an article by The New York Times, with C. J. Chivers and Eric Schmitt being the article’s authors. The story in the article reveals that during the war in Syria, the Saudis had purchased certain weapons from Croatia, and then shipped them to Jordan, from where it was smuggled into Syria to support the free Syrian Army in their fight against President Bashar al-Assad (Chivers and Scmitt 2013). Although, this news was officially denied by both the Croatian and Saudi government, its authenticity was proved through the data collected by Higgins. It all began during 2012 when Higgins, an unemployed finance professional and prospective journalist, formed a blog to write about the civil war in Syria. His early posts in the blog were mainly unorganised compilation of videos, which he had viewed on Youtube, Facebook, and Twitter. However, after few months, he incorporated a more systematic methodology to his act of viewing, collecting, and examining war-related videos coming from Syria. Although, he does not understand or speak Arabic, never been to Middle-East (let alone Syria), and had no formal training in arms and ammunitions or intelligence activities, Higgins was able to find patterns regarding the usage of Croatian weapons in the conflict. In addition, Higgins has also uncovered evidence regarding the use of cluster bombs by the Syrian army. As above-mentioned, he came up with all these findings only by exploiting the publicly accessible data in the web, that too from the confines of his home in Leicester thereby exhibiting the significance of Big Data. “An unemployed finance and admin worker at the time, Higgins was held up as an example of what can happen when we take advantage of the enormous amount of information being spread across the internet every day” (Moran 2014). The ways Journalists manage to Research Vast amounts of Data When one focuses on how journalists manage to research vast amounts of data, analyze them, find patterns, and come up with a story, it provides key perspectives. Bradshaw (2010) explains the workflow of this data-driven journalism by categorizing it into four steps namely finding the data, interrogating data, visualizing data, and mashing data. Journalists can find relevant data using their expert knowledge or through their good contacts. The interrogation of the gathered data requires an excellent understanding of the technical jargon in the data as well as grasping the wider context of the data. Visualization and the eventual mashing of the data for writing the news story involve the work of not only the journalists but also designers who visualize the data using number of softwares. So, it is evident that from collection of data to writing a story, there are number of steps involved and all the steps have to be carried out through teamwork. As Bradhsaw (2010) points out “The reality is that almost no one is doing all of that”. Gladwell (2010) validates this perspective by stating that “articulation of a coherent design philosophy is best handled by a sprawling, leaderless organizational system”. However, the key point is that at the end of this workflow, the collected raw data has to be accessible and readable to the eventual readers. “As a result the value to the public grows, especially when complex facts are boiled down into a clear story that people can easily understand and remember” (Baack 2011, p.3). So, the journalists have to follow the process of refinement or filtering, where they have to filter the raw data and find certain compelling patterns which can be transformed into a meaningful news story to the readers. When it comes to gathering of data, there is sizable amount of publicly relevant data in the United States and Britain because of the open government initiative. However, the problem is refining or filtering the needed data. In the case of huge data, it is difficult to find patterns in it. So, the needed data has to be refined using free tools such as IBM’s ManyEyes and by making it interactive using web applications. This problem of refining happened to newspapers and journalists when they had access to about 92,000 documents from the leaked War Logs of Afghanistan conflict. The journalists found it difficult and time-consuming to go through all the documents individually and then writing about the findings. As above-mentioned, this problem was managed by the newspapers especially The Guardian and The New York Times. “Tools were used to go through the data and to create visualizations and interactive web application which made the material accessible for readers” (Baack 2011, p.2). The key problem with this source of leaked government data is that it cannot be continuous phenomenon. That is, individuals or governments might not release or leak sensitive documents regularly. So, it would be viable for journalists not to wait for the leak or release of government data, and instead develop their own trusted data hubs in line with the data-driven journalism. “They should not only focus on handling externally produced data sets, but also develop and structure their own, internal database” (Baack 2011, p.8). The ways Eliot Higgins managed to research vast amounts of data In that direction only, Higgins developed his own internal database, using and analyzing data mainly from the web. As pointed above, he started viewing videos and photos of Syrian conflict in the Youtube, followed by Facebook and Twitter as well. These videos and photos were uploaded by activists, rebel brigades, Islamist groups, as well as from Assad supporters and state TV footage (Weaver 2013). When Higgins started to systematically view these contents from May 2012, certain patterns emerged regarding the weapons. Higgins himself acknowledged this fact, “Basically, people see a video and they think: anyone could have uploaded that. I see the video and say: let’s find the data, Facebook page, Twitter account, find out the details of the YouTube channel, see if there’s a consistent pattern” (Hoyos 2015). Furthermore, he subscribed to more than 500 Youtube channels thereby receiving constant flow of new videos from various sources. He regularly checked every channel and built a picture of events that are happening on the ground, particularly tracking all the weapons that were used by both the sides, where it was used, and other details. While monitoring the content, he came across four unusual weapons in early 2013. To find the origins of these weapons, Higgins followed number of effective steps. First, he searched for those weapons on the web using the screenshots from the video. He compared those images with the images published by the weapons’ manufacturers and other weapons enthusiasts. “Manufacturers of these weapons sometimes put detailed images of their products online, and one useful source of images of aircraft weaponry are people who visit air shows and then post images of the weapons on internet forums” (Higgins 2013). When he is not able to find matching images of the weapons in the web, he tried googling with the markings on the weapon. Although, he might have found only one component of a much larger weapon, the markings on that particular component facilitated him to discover the origins and other details of that weapon. “It turned out these arms were the M60 recoilless gun and M79 Osa from the former Yugoslavia; the Soviet RPG-22, used by a number of countries, including Croatia; and the Croatian RBG-6 grenade launcher” (Higgins 2013). When Higgins viewed many videos and as that pattern on weapons continued, he started adding the weapons’ details in a spreadsheet specifying the weapon type, group using them, location, and map coordinates. When he filtered and analyzed these details, he found out that these weapons of Croatian origin were mainly appearing in the town of Busar al-Harir, Daraa, with the Free Syrian Army using as part of their offensive against the al-Assad’s forces (Higgins 2013). After collecting a strong basic data and thereby coming up with certain hypothesis, Higgins used the services of few people in Syria to confirm his hypothesis. That is, through his reporting on his blog, “he gradually developed a trust with people in Syria, who will photograph particular bomb remnants for him” (Hoyos 2015). Most of these collaborations with the Syrian people were done online thereby reflecting virtual connectivity (Allan and Thorsen 2009). Although, he does not speak Arabic, there were number of people who were willing to translate the local language in the videos into English for him. So, it is evident that what started as a disorganized posting of videos turned out to be a systematic collection, filtering, and refining of videos, through which he found certain patterns regarding the use of Croatian weapons, thereby publishing it his blog, and further developing his blog with collaboration from Syrian people as well. After publishing his findings on his blog, Higgins went to The New York Times, who published an article summarising what he has found. Data Journalism vs. Traditional journalism and conclusions When this data journalism is compared with traditional journalism, certain differences emerge. The first key difference is that data-driven journalism can be carried out within the confines of an office or home, without being on the field and collecting the data first-hand. As pointed out earlier, Higgins did not enter Syria, nor seek permission of the authorities, or wear bullet-proof vest, dodge bullets, and others in line with the activities of traditional journalism. “He demonstrates that the skills needed for journalism today are not necessarily those taught at university, in a computer lab or in a newsroom briefing. He needs no desk, no employer to send his pay cheque and no passport or flak jacket to report on distant battle fields” (Hoyos 2015). The second difference is that certain news stories cannot be found through traditional journalism. That is, as Higgins himself pointed out, journalist working on the ground in Syria might not have picked up this issue of Croatian weapons because they might have missed the markings and other significance of these weapons. So, because of data journalism, “he had a much better picture of what was going on in Syria, than a journalist based locally” (Pasin 2014). To conclude, although data-driven journalism is being sizably used, it cannot sideline the traditional journalism because of the latter’s extensiveness. Instead, both can coexist with the data journalism enhancing the traditional journalism. This perspective was validated by Higgins himself who stated that there is no substitute for traditional war reporting and that “cant replace journalists on the ground,” instead this data journalism can compliment and direct them (Weaver 2013). So, it is clear that data gleaned from open sources, particularly content from the web, can provide deep insights into various world events. Although, gathering the data, filtering or refining them, analysing them, and finding patterns for a news story might be a cumbersome task, it can lead to exclusive, interesting, and even thought-provoking news stories. References Allan, S and Thorsen, E., 2009. Citizen Journalism: Global Perspectives. Peter Lang, New York. Baack S., 2011. A New Style of News Reporting: Wikileaks and Data-driven Journalism. Available from http://www.ssoar.info/ssoar/bitstream/handle/document/40025/ssoar-cyborgsub-2011- baack-A_new_style_of_news.pdf?sequence=1(accessed on April 16, 2015) Bradshaw, P., 2010. How to be a Data Journalist. Available from http://www.guardian.co.uk/news/datablog/2010/oct/01/data-journalism-how-to- guide(accessed on April 16, 2015) Chivers, CJ and Scmitt, E., 2013. Saudis Step Up Help for Rebels in Syria With Croatian Arms. Available from http://www.nytimes.com/2013/02/26/world/middleeast/in-shift-saudis-are-said-to-arm- rebels-in-syria.html(accessed on April 16, 2015) Gladwell, M., 2010. Small Change. Available from http://www.newyorker.com/magazine/2010/10/04/small-change- 3#ixzz15tWotpsd(accessed on April 16, 2015) Higgins, E., 2013. Social Media and the Conflict in Syria. Available from http://schoolofdata.org/2013/08/23/social-media-syria/(accessed on April 16, 2015) Hoyos, C., 2015. Eliot Higgins: the Sleuth on the Sofa, Armed with Determination and a Laptop. Available from http://www.ft.com/cms/s/0/20e7dbd2-b080-11e4-92b6-00144feab7de.html(accessed on April 16, 2015) Moran, M., 2014. Big Data brings New Power to Open-Source Intelligence. Available from http://theconversation.com/big-data-brings-new-power-to-open-source-intelligence- 26554(accessed on April 16, 2015) Pasin, M., 2014. 3 Great Examples of Data Journalism Stories. Available from http://www.analyticsforfun.com/2014/07/3-great-examples-of-data- journalism.html(accessed on April 16, 2015) Weaver, M., 2013. How Brown Moses exposed Syrian arms trafficking from his front room. Available from http://www.theguardian.com/world/2013/mar/21/frontroom-blogger-analyses-weapons- syria-frontline(accessed on April 16, 2015) Read More