1. From a machine learning perspective, journalism is *simply* event detection applied to continuously updating data.
  2. Let’s look at an hypothetical example where there’s a lot of reliable data: Sports. If we were to monitor all the professional soccer/football matches around the world, occurring over a given weekend, how do we determine what the most newsworthy games were?
  3. That decision is traditionally made by a journalist based on their domain knowledge and context of the subject — they may emphasize matches that had the biggest offset, focus on top ranked teams, or even pay more attention to teams where highly paid…


This is why journalism must evolve into becoming an “Information Science”

1. The pandemic forced many news organizations to innovate how they use technology, rethink how they manage remote staff and make sense of huge amounts of complex information. Covid also demonstrated the importance of tracking data in real-time and making it a central reference for all reporting!

2. The sad reality is that the frequency of how often pandemics occur will only accelerate with climate change, which means newsrooms will need to be at least 10x more efficient in how they collect data — or risk their very own survival.

3. As temperatures rise and the global population grows to…


1.A key premise of computational journalism is that news events can be explained with the same rigor used by scientists to study the natural world. Journalism is going through a process of “mathematization” and eventually evolve into *Information Science*

2.The explosion of data from the web, sensors, mobile devices and satellites, combined with the accessibility to machine learning, creates the ideal environment for transforming how all information around us is sourced.

3. Advances in AI research are already allowing computational journalists to measure issues that were previously impossible to quantify (like counting trees in a desert), and as a result…


The crisis has shown a secondary, positive side effect: accelerating the speed of journalistic innovation.

The growing demand for information during the pandemic has not been met with an adequate increase in journalistic resources. In fact, the opposite has been true. According to the Poynter Institute, in the US alone over 33,000 journalists have been laid off, furloughed, or given pay cuts as a direct consequence of the coronavirus, resulting in a reduced capacity to cover fundamental public health issues. Despite the dire situation, the crisis has shown a secondary, positive side effect: accelerating the speed of journalistic innovation.

Take the case of British local wire service Radar AI. The joint venture between Press Association…


Data scientists are working alongside journalists to explore how well-established machine learning methods can help to easily find gaps in editorial coverage.

Coverage Map is a tool that can find opportunities in WSJ’s editorial coverage and audience impact by using deep learning.

Only a few years ago, using artificial intelligence in journalism was cutting edge, but nowadays it is quickly entering the workflow of a growing number of news organizations. The knowledge around machine learning methods is becoming more accessible as the cost of AI projects has gone down since many models are readily available online and can be implemented — even by small newsrooms.

Using these models, our team of data scientists took on, at first sight, an enormous challenge: analyzing and deriving insights from a decade’s worth of WSJ articles. …


The Wall Street Journal is experimenting with a new approach for reporting how smart algorithms work, beyond simply describing them.

Image Credit: Gabriel Gianordoli/ WSJ

Journalists, who routinely ask questions of their sources, should also be asking questions about an algorithm’s methodology. The rules created for algorithms need to be explicit and understood. The Wall Street Journal has been experimenting with a new approach to explain how AI works by letting readers experiment with it.

“Interactive graphics can provide insights into how algorithms work in a way beyond simply describing its output. They can do this by acting as safe spaces in which readers can experiment with different inputs and immediately see how the computer might respond to it,” said deputy graphics director Elliot Bentley.


Developing “extreme future scenarios” can help media leaders drive cultural change and implement sustainable newsroom innovation.

Four extreme scenarios developed in the context of AP’s futurecasting exercise.

A strategic planning technique, “futurecasting” enables organizations to make decisions about where to focus their growth efforts. At the Associated Press (where I work), we’ve used it to consider an array of provocative — yet plausible — alternative futures that provide the necessary context to explore new opportunities. We collaborated with leading innovation agency Frog Design to imagine the future of the news industry, drive cultural change and embrace innovation.

“It’s essential to look ahead and anticipate change,” said Jaime Holguin, AP’s manager of news development. …


The Tow Center for Digital Journalism at Columbia Journalism School hosted an artificial intelligence and journalism workshop where particpants learned how to create AI-driven text and video stories.

AP strategist and Tow Fellow Francesco Marconi introduces students to the principles of “Augmented Journalism”

As news consumption patterns change, reporters need to develop new storytelling approaches and master the use of new tools, many of which are now powered by artificial intelligence.

Implementing these smart machines in the newsroom will still demand editorial expertise, though. Because while content can be generated automatically, journalists in new roles such as “automation editors” will need to watch for and correct any errors.

“The current generation of successful automation technology scales the output of human labor, rather than replacing it entirely,” said Nick Haynes, a data scientist at Automated Insights who helped teach the workshop.


Live Like Fiction: thank you for helping turn my blog posts into a book!

Dear Friends,

As a few of you may already know, I was given the opportunity to turn my viral blog posts here on Medium into a book called Live Like Fiction. It’s filled with inspiring stories, practical strategies and thought-provoking activities to help uncover the best version of yourself.🙌

The book will be released mid-July. In the interim, I am starting this newsletter to share with you stories that combine purpose, inspiration and storytelling. These are the ingredients you will need to turn your dreams into…


While artificial intelligence can help newsrooms build a consistent fake news detector, it can also empower others to disseminate and even create new forms of misinformation.

Fake news is nothing new. The Roman Emperor Augustus led a campaign of misinformation against Mark Antony, a rival politician and general. The KGB used disinformation throughout the Cold War to enhance its political standing. Today fake news continues to serve as a political tool around the world, and new technologies are enabling individuals to propagate that fake news at unprecedented rates.

One of those new developments, artificial intelligence, can help journalists build a consistent fake news detector, but AI can also empower others to disseminate and even create new forms of misinformation. …

Francesco Marconi

Computational journalist and co-founder of Applied XL. I write about data science, storytelling and innovation.

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