Data journalism uses digital tools to simplify the data collection process, and it has been around in some form since the 1950s. The big difference today is that today’s world involves heavy usage of number crunching and probability.
With data journalism emerging, news networks have switched their old ways in favour of data processing in greater amounts to make news faster and provide greater depth than at any time previously.
is the infinite pool of constantly evolving and massing data owned by no one person in particular – just like the internet. With the correct tools and personnel, is something that can be leveraged for vast arrays of complex functions such as personality profiling and the improvement of public healthcare. The use of big data for media for enhancement and quantity of journalism is beginning to be recognised.
The biggest difference between pedestrian, TRP-chasing reporting and data-driven journalism is that data-driven journalism requires keeping a close eye on the problems facing the masses.
When these are discovered, they can be broadcasted to a worldwide audience to get resolutions quicker. Social media trends relating to a given region provide a fairly accurate indication of the public there, and social media’s position in historical events – such as the Arab spring – is undeniable.
Facebook, Twitter and other sites were all utilised to mobilize troops and coordinate the ousting of strongmen heads of state in the rebellion, and many believe the social media sites to be more resourceful than the news networks in spreading information fast between places.
This highlights a need for news networks to harness big data power in social media and public forums.
Several news networks lean favourably between the left and right wings, and centralist networks are extremely slim. This polarization makes viewers take sides and pick on those who do not follow their line of thinking and opinion.
This mentality may draw some profit for the news networks, but it fractures society the more it happens. News networks and publications can adopt machine learning and AI to make data journalism unbiased and centrist, being used for content curation and publishing.
Machine learning utilises pattern recognition of unique and indiscernible trends throughout large chunks of data. AI applications can therefore differentiate factual and fabricated data, separating them so that data journalists can present fact-based articles and news reports.
Although a larger percentage of news consumers prefer a left-wing/right-wing separation, a non-biased path is a better option. If the media across the board adopted the same line of neutrality, there would be a better focus on figures such as heads of state and politicians focusing on performance and development instead of deflection of blame from poor governance.
Machine learning,and AI complement human journalists to eliminate human error in newsmaking, effectively making journalism driven more by integrity.