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Data Journalism: How Big Data-Driven Analytics Improves Newsmaking

You may find the word “data” in the term “data journalism” redundant. After all, newsmaking of any kind, even the tacky, propaganda-driven type, has always relied heavily on data. Earlier, on-set journalists, reporters and data collection teams would scramble to procure information that could then be processed before being presented to the masses. However, this approach had an obvious problem—the disparities between newer real-life developments and published news reports in newspapers or even on electronic media would be vast.

Data journalism—newsmaking driven by faster data collection and visualization, traces of which were first witnessed in the US during the 1950s—uses digital tools to simplify data collection. Data journalism involves heavy usage of probability and number crunching. For example, those aspects are evident in this insightful piece about Edward Snowden’s leaked NSA files published on TheGuardian.com. With the emergence of data journalism, news networks have swapped their old handicams with laptops and smartphone cameras, with data processing assuming greater prominence as compared to data collection.

Big data promises to make news even faster and possess greater depth than ever before. Big data, as you know, is an infinite pool of constantly evolving and growing data owned by nobody in particular, just like the internet. With the right tools and personnel, big data can be leveraged for a vast array of complex functions, such as personality-based psychometric profiling and improving public healthcare in smart cities. Some of the use cases involving big data in media for the enhancement of the quality and quantity of journalism are listed below:

Gauging Public Opinion from Online Platforms

One of the main differences between pedestrian, TRP-chasing reporting and responsible data-driven journalism is that the latter involves keeping a close eye on the problems faced by the masses. Once such issues are discovered, they can be broadcasted to a massive audience so that their resolutions can arrive quickly.

Needless to say, social media trends of a given region offer a fairly accurate reflection of the pulse of the general public there. Social media’s role in many of this century’s historical events is undeniable. Take the Arab Spring, for example. The rebellions, which primarily centered around the ousters of strongmen heads of state, such as Muammar Gaddafi, Hosni Mubarak and Ali Abdullah Saleh, involved the use of Facebook, Twitter and a few other sites for mobilization of troops and co-ordination. In fact, many believe that social media sites were more helpful than news channels when it came to the spread of information from one place to another. Therefore, news networks need to find new ways and resources to harness the big data-based power of social media sites as well as public forums such as Quora.

AI-powered applications leveraging big data in media can scan through millions of social media posts and forums. The data scraped from such sites is then fed into sentiment evaluating applications used to harness big data in media. Sentiment-based big data in media can uncover the underlying issues of a given region by determining the mood of social media posts, forum posts, e-mails to public offices and other sources of information. In such posts, aspects such as the negativity (or positivity) in sentences used, choice of words, length and readability of posts and characteristics of images or other media within the posts are assessed to determine the mood of the public in a given region. Currently, sentiment-based tools using big data in media are still in their nascent stages. Once the technology develops adequately and is more practical to implement for journalism purposes, news networks can use it to bring the problems of citizens in front of the ruling government.

Eliminating Bias from Mainstream Journalism

As implied earlier, several news channels in today’s polarizing times seem to lean towards either left or right-wing ideologies, with centrist networks seemingly non-existent. What’s more, this kind of polarization in news coverage means that viewers also enjoy taking sides and picking on those who do not agree with their line of thinking and opinions. While this may be profitable for news channels from a TRP point of view, it can lead to a fractured society in the long term if not dealt with in its initial stages.

To make data journalism unbiased and centrist, news publications and networks can use machine learning and AI for content curation and publishing. Machine learning, as you know, involves pattern recognition of unique and indiscernible trends within large amounts of data. This allows AI-based applications to clearly differentiate between factual information and fabricated data. Once factual data is separated from the rest, data journalists can create factually correct articles and news reports. Furthermore, such content can be edited in a way that it does not sound or read too biased. Such an approach to creating news campaigns makes newsmaking more ethical. Moreover, it also forces more and more reporters and journalists to choose the path of integrity when it comes to information dissemination for thousands and thousands of viewers or readers.

Although a large number of news consumers prefer to have left and right-leaning news networks, following a non-biased path is a definitively better option. Often, political parties and politicians use their ideologies as a cover to deflect blame from poor governance, financial performance and policies. If the media, across the board in a given country, follows a neutral line, the heads of state and politicians will have to focus on performance and development.

Automating Financial Management, Autonomous Reporting and Piece Writing

AI’s influence isn’t just limited to newsmaking in a given news network. The technology, along with big data and NLP, also enables media houses to manage their financial operations more efficiently. These technologies make the analysis of financial documentation easier and, as a result, lead businesses in media to rake in profits through data-driven financial management and expense control. AI-based accounting systems can not only detect instances of fraud and errors in accounting statements, but also reduce the number of false positives in fraud detection. The reduction of false positives, in turn, minimizes the expenses that otherwise would be spent on fraud-related financial investigations. Media houses are generally closely watched by the IRS and other public finance inspection bodies. A well-calibrated financial management framework ensured by AI and big data in media and journalism keeps news networks in a safe space when it comes to complying with financial regulations.

However, perhaps the most revolutionary change big data analytics will bring in mainstream journalism is the automation of newsmaking. An example of this is the Quakebot, an application that can write automated articles related to earthquakes and other tectonic issues by scanning through constantly-updating information on the US geological records. AI-based “writers” make the creation of articles entirely autonomous after data-driven content curation and bias-removal processes.

The concept of robotic journalists and on-field reporters also comes with several benefits. For example, covering dangerous regions, such as active war zones or places affected by natural disasters, can be made less dangerous with robotic reporters. Such tools eliminate the chances of journalists losing their lives. Big data in media and journalism is useful for the constant training and improvement of such AI-based applications. Apart from these two types of automation use cases, news networks can employ machine learning for other purposes too—such as automated interviews, debates and discussion conduction.

One of the often-repeated lines of involving AI in journalism is that the technology will replace human journalists, a statement that simply cannot be true. Machine learning, big data analytics and AI will complement human journalists and eliminate human error from newsmaking, making journalism more integrity-driven. News media is rightly considered to be the fourth pillar of any democracy. To strengthen the society, data journalism has to hold this end up and keep the democracy of a country healthy and functional. The emergence of big data in media in journalism promises to do exactly that.