Data Journalism: How Big Data-Driven Analytics Improves Newsmaking | 7wData

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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:

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 datain 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.

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.