What is Algorithmic Journalism?
Published: 04:07 PM,Jul 03,2025 | EDITED : 08:07 PM,Jul 03,2025
Media
At its core, algorithmic journalism uses data analysis and machine learning tools to automate parts of news production. These algorithms can sift through vast amounts of data—like financial figures, sports scores, or statistical information—and compile it into news stories without much human input. For example, a report about a sports game or stock market movement can be generated automatically by a computer interpreting raw data.
It’s not just about producing stories; algorithms can also personalise news feeds, recommend articles, and even detect trending topics in real-time. This makes the news more dynamic and accessible, with automation handling the repetitive or data-heavy parts of journalism.
HOW IT WORKS
The process looks something like this:
Data collection: The algorithm pulls in relevant data from various sources—scores, financial reports, weather stats, etc.
Processing: It analyses this data to find patterns and key insights.
Content creation: Based on templates and rules, the algorithm generates written summaries, charts, or multimedia content.
Distribution: The finished content is then delivered directly to users through websites, apps, or social media platforms, often personalised based on user preferences.
For example, an algorithm might automatically generate a short report about a sports game, complete with scores and highlights, seconds after the game ends. This speeds up news reporting and reduces the workload for journalists.
Applications in Big Media Networks
Print Media:
Traditionally, print outlets relied heavily on human writers. However, some papers are now experimenting with automation for routine content. The Associated Press has been using algorithms to produce earnings reports and financial news since 2014. This allows journalists to focus on more complex stories, while routine reports are generated efficiently and accurately.
Television News:
While live reporting needs human touch, some TV outlets use algorithms for data visualisation and summarising stories. For instance, CNN has employed machine learning to automatically generate graphics and captions, making news segments more engaging. During the COVID-19 pandemic, CNN also used data-driven visualisations to show infection rates and trends in real-time.
Digital and Online Media:
This is arguably where algorithmic journalism has had the biggest impact. Many news websites and apps now personalise content using algorithms. The BBC uses algorithms to recommend stories based on a user’s browsing history, encouraging engagement and retention. Similarly, The Guardian employs machine learning to analyse user comments and feedback, helping them understand reader sentiment better.
Major media players like Bloomberg and Yahoo News have fully embraced algorithmic content, with Bloomberg’s use of its Cyborg Engine automatically generating thousands of earnings stories, freeing up journalists for investigative work.
According to a 2023 report by The Reuters Institute for the Study of Journalism, nearly 60% of global news organisations are now integrating some form of automation. The industry sees it as a way to handle the exponential growth of data and meet the demand for faster, more personalised content. However, it’s not without challenges. Ethical issues about misinformation, bias in algorithms, and job displacement are often discussed.
The BBC has stated that their use of algorithms is designed to complement journalism, not replace it. They emphasise transparency and fact-checking in automated content, aiming to maintain trustworthiness—an essential factor in today’s media landscape.
It’s not just about producing stories; algorithms can also personalise news feeds, recommend articles, and even detect trending topics in real-time. This makes the news more dynamic and accessible, with automation handling the repetitive or data-heavy parts of journalism.
HOW IT WORKS
The process looks something like this:
Data collection: The algorithm pulls in relevant data from various sources—scores, financial reports, weather stats, etc.
Processing: It analyses this data to find patterns and key insights.
Content creation: Based on templates and rules, the algorithm generates written summaries, charts, or multimedia content.
Distribution: The finished content is then delivered directly to users through websites, apps, or social media platforms, often personalised based on user preferences.
For example, an algorithm might automatically generate a short report about a sports game, complete with scores and highlights, seconds after the game ends. This speeds up news reporting and reduces the workload for journalists.
Applications in Big Media Networks
Print Media:
Traditionally, print outlets relied heavily on human writers. However, some papers are now experimenting with automation for routine content. The Associated Press has been using algorithms to produce earnings reports and financial news since 2014. This allows journalists to focus on more complex stories, while routine reports are generated efficiently and accurately.
Television News:
While live reporting needs human touch, some TV outlets use algorithms for data visualisation and summarising stories. For instance, CNN has employed machine learning to automatically generate graphics and captions, making news segments more engaging. During the COVID-19 pandemic, CNN also used data-driven visualisations to show infection rates and trends in real-time.
Digital and Online Media:
This is arguably where algorithmic journalism has had the biggest impact. Many news websites and apps now personalise content using algorithms. The BBC uses algorithms to recommend stories based on a user’s browsing history, encouraging engagement and retention. Similarly, The Guardian employs machine learning to analyse user comments and feedback, helping them understand reader sentiment better.
Major media players like Bloomberg and Yahoo News have fully embraced algorithmic content, with Bloomberg’s use of its Cyborg Engine automatically generating thousands of earnings stories, freeing up journalists for investigative work.
According to a 2023 report by The Reuters Institute for the Study of Journalism, nearly 60% of global news organisations are now integrating some form of automation. The industry sees it as a way to handle the exponential growth of data and meet the demand for faster, more personalised content. However, it’s not without challenges. Ethical issues about misinformation, bias in algorithms, and job displacement are often discussed.
The BBC has stated that their use of algorithms is designed to complement journalism, not replace it. They emphasise transparency and fact-checking in automated content, aiming to maintain trustworthiness—an essential factor in today’s media landscape.