Artificial Intelligence is everywhere, gradually hitting the newsrooms and the societies. The first wave of artificial intelligence created ripples in the newsrooms with chatbots, automated reporting systems and machine learning techniques. They have been created to minimise the efforts within a newsroom, thus, helping to write initial news reports.
Google provided British News Agency Press Association a sum of $805,000 to build a software that will gather, automate and write nearly 30,000 local stories a month. The software known as RADAR (Reporters and Data and Reports), will automate local reporting with large public databases from government agencies or local law enforcement.
South Korea’s Yonhap news agency has created an automated reporting system ‘Soccerbot’ to produce news on football games.
Similarly, Reuters has created its own automation tool known as ‘Reuters News Tracer’ to combat the problem of fake news.
Content today is made to suit for everybody in the world. A journalist writes stories hoping to reach as many people as possible.
And with such a huge world to serve, the complexities increase. This is where the automation and creation of artificial intelligence framework plays a major role. Organisations like Google, Yahoo, Facebook have already integrated with AI technologies to cement for the future.
Let us also have a look at the statistics showing the usage of artificial intelligence by the newsrooms and why.
Thomson Reuters built a software capable to generate a piece of poetry by pairing human intelligence and machine learning. They took the first prize at the annual Turing Tests competition which rewarded the computer generated artworks. While the machine generated output won the dance, poetry and music categories, there was no winner for literature category.
Here, we can draw some essential facts about artificial intelligence in journalism,
Cumulatively, we can say that, artificial intelligence helps majorly in minimising the job in a newsroom, but still it requires human assistance to judge the quality of the news.
Newsworthy events that happens all the time over the internet like MeeToo, Quora posts, etc. should first reach the newsrooms to report. Therefore, artificial intelligence can crawl over the web content in real time and identify newsworthy content.
Newsrooms function as per their readers’ needs which can be mainly used to decide how news should be shared. For example, your Facebook page can use machine learning to decide which time of the day a certain content should be posted in order to maximize clicks and views.
Automated writing has some distance to cover, but artificial intelligence tools to assist reporters are already in use. Like while writing an article about a recent football game, few related pieces can be suggested automatically. This suggestion should be based on the popularity of the news content which has to be analysed artificially.
Certain articles related to science and surveys are often seem to be misleading. Detecting them automatically before publishing would be very beneficial for an organisation. In order, to eradicate the trend of fake/misleading news, Reuters have developed a news tracer, Reuters News Tracer.
News app mostly have push notifications. Using machine learning it decides what content the user likes, how many notifications are ideal for the user, when would the user click on them, etc.. This machine translation method has a huge application in personalisation of content.
For example, Hillary Clinton in an interview says ‘There have been more than 270 mass shootings in the US last year. And only four or five were killed.’ Here, technically the exact statement should have been ‘four or five were killed or injured in each mass shooting’. This misstated speech changed the entire threshold of the facet.
Therefore, real time detection of possible falsehood in interviews will make the viewers more informed and newsrooms profitable.
Artificial intelligence and machine learning are the smartest ways to overcome the issue of mentioning hurtful and offensive comments in the comment section of an article. The newsroom publishers can now provide real time feedback to the commentators. One such example is Google’s Perspective which determines the ‘toxicity’ of the comment.
Nova Techset and Taylor & Francis Journal have created a new copy editing process called Contextual Copy editing which uses AI and natural language processing (NLP) to assess and score the language quality of the articles accepted into the Taylor & Francis journals workflow to determine a customized level of editorial intervention.
Aries Systems Corporations have integrated with Meta Bibliometric Intelligence into Editorial manager for peer review tracking. The technology created by Meta, applies AI towards the identification of high impact manuscripts at first submission, allowing editors to triage and rank incoming manuscripts.
Now, how soon do you think artificial intelligence can be a positive force in journalism? The path isn’t certain but getting to the right answers and actions would require close collaboration between the technology, newsrooms and journalists.
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