Paul teaches data journalism at Birmingham City University and is the author of a number of books and book chapters about online journalism and the internet, including the Online Journalism Handbook, Mobile-First Journalism, Finding Stories in Spreadsheets, Data Journalism Heist and Scraping for Journalists.
From 2010-2015 he was a Visiting Professor in Online Journalism at City University London and from 2009-2014 he ran Help Me Investigate, an award-winning platform for collaborative investigative journalism. Since 2015 he has worked with the BBC England and BBC Shared Data Units based in Birmingham, UK. He also advises and delivers training to a number of media organisations.
Generative AI can be used at all points in the journalism process: this post focuses on pre-production
Last week I delivered a session at the Centre for Investigative Journalism Summer School about using generative AI tools such as ChatGPT and Google Gemini for investigations. In the first of a series of posts from the talk, here are my tips on using those tools for idea generation.
Generative AI tools may not be entirely reliable, but that doesn’t mean that they’re not useful. Journalism, after all, is about more than just gathering information: reporters also need to generate story ideas, identify and approach potential sources, plan ahead, write and edit stories and solve a range of technical challenges. All of these are areas where genAI can help.
Machine learning and Natural Language Processing (NLP) are two forms of artificial intelligence that have been used for years within journalism. In this video, part of a series of video posts made for students on the MA in Data Journalism at Birmingham City University, I explain how both technologies have been used in journalism, the challenges that journalists face in using them, and the various concepts and jargon you will come across in the field.
SRF Data example (note: this uses a Random Forest algorithm, which employs a collection of ‘decision trees’, and is not a decision tree as stated in the video)
Having outlined the range of ways in which artificial intelligence has been applied to journalistic investigations in a previous post, some clear challenges emerge. In this second part of a forthcoming book chapter, I look at those challenges and other themes: from accuracy and bias to resources and explainability.
Investigative journalists have been among the earliest adopters of artificial intelligence in the newsroom, and pioneered some of its most compelling — and award-winning — applications. In this first part of a draft book chapter, I look at the different branches of AI and how they’ve been used in a range of investigations.
Virtual reality and augmented reality have opened up a range of new opportunities for journalists and publishers — as well as new challenges.
In this video, made for students on the MA in Data Journalism and the MA in Media Production at Birmingham City University, I explain what types of stories and projects suit these technologies, what to consider when using them, and some useful techniques from those who have worked in the field.
Explainers are one of the most widely used forms of ‘evergreen’ content. In this unpublished extract from the latest edition of the Online Journalism Handbook, removed due to word limit, I explore why they are so popular, what types of subject are suitable, and how explainers are structured.
A few months ago I delivered a webinar for the European Data Journalism Network and DataNinja about the range of ways that journalists can use ChatGPT and other generative AI tools — from idea generation and mapping systems to help with spelling and coding — and what issues they need to be aware of.
The video is now available online and you can watch it below.
In this latest post in the FAQ series, I’ve been asked to help answer a question on the “ethical dilemmas faced by news organisations when considering the use of AI in reporting stories“.
Planning an investigation, or any larger editorial project, raises its own particular challenges — but if you know where to look, you can find resources that are especially useful in anticipating and tackling those.
The most basic change to the Inverted Pyramid of Data Journalism is the recognition of a stage that precedes all others — idea generation — labelled ‘Conceive’ in the diagram above.
This is often a major stumbling block to people starting out with data journalism, and I’ve written a lot about it in recent years (see below for a full list).
The second major change is to make questioning more explicit as a process that (should) take place through all stages — not just in data analysis but in the way we question our sources, our ideas, and the reliability of the data itself.
A third change is to remove the ‘socialise‘ option from the communication pyramid: in conversation with Alexandra Stark I realised that this is covered sufficiently by the ‘utilise’ stage (i.e. making something useful socially).
Alongside the updated pyramid I’ve been using for the past few years I also wanted to round up links to a number of resources that relate to each stage. Here they are…
Online Journalism Blog 