Strong factual storytelling relies on good idea development. 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 to generate good ideas by avoiding common mistakes, applying professional techniques and considering your audience.
The links mentioned in the video include:
Related post: Here’s how we teach creativity in journalism (and why it’s the 5th habit of successful journalists)
In the latest in a series of posts on using generative AI, I look at how tools such as ChatGPT and Claude.ai can help help identify potential bias and check story drafts against relevant guidelines.
We are all biased — it’s human nature. It’s the reason stories are edited; it’s the reason that guidelines require journalists to stick to the facts, to be objective, and to seek a right of reply. But as the Columbia Journalism Review noted two decades ago: “Ask ten journalists what objectivity means and you’ll get ten different answers.”
Generative AI is notoriously biased itself — but it has also been trained on more material on bias than any human likely has. So, unlike a biased human, when you explicitly ask it to identify bias in your own reporting, it can perform surprisingly well.
It can also be very effective in helping us consider how relevant guidelines might be applied to our reporting — a checkpoint in our reporting that should be just as baked-in as the right of reply.
In this post I’ll go through some template prompts and tips on each. First, a recap of the rules of thumb I introduced in the previous post.
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In the fourth of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (the first part covered idea generation; the second research; the third spreadsheets), I look at using generative AI tools such as ChatGPT and Google Gemini to help with scraping.
One of the most common reasons a journalist might need to learn to code is scraping: compiling information from across multiple webpages, or from one page across a period of time.
But scraping is tricky: it requires time learning some coding basics, and then further time learning how to tackle the particular problems that a specific scraping task involves. If the scraping challenge is anything but simple, you will need help to overcome trickier obstacles.
Large language models (LLMs) like ChatGPT are especially good at providing this help because writing code is a language challenge, and material about coding makes up a significant amount of the material that these models have been trained on.
This can make a big difference in learning to code: in the first year that I incorporated ChatGPT into my data journalism Masters at Birmingham City University I noticed that students were able to write more advanced scrapers earlier than previously — and also that students were less likely to abandon their attempts at coding.
You can also start scraping pretty quickly with the right prompts (Google Colab allows you to run Python code within Google Drive). Here are some tips on how to do so…
Continue readingGenerative AI tools like ChatGPT and Gemini can be a big help when dealing with data in spreadsheets. In this third of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (the first part covered idea generation; the second research), I outline tips and techniques for using those tools to help with spreadsheet formulae and reshaping data.
Whether you come across data as part of story research, or compile data yourself, chances are that at some point you will need to write a formula to ask questions of that data, or make it possible to ask questions (such as creating a column which extracts data from another).
If you find yourself coming up against the limits of your spreadsheet knowledge, then genAI tools can be useful both in breaking through those — while expanding your knowledge of functions and formula writing.
In the second of a series of posts from a workshop at the Centre for Investigative Journalism Summer School (read the first part on idea generation here), I look at using generative AI tools such as ChatGPT and Google Gemini to improve sourcing and story research.
Research is arguably the second-highest risk area (after content generation) for using generative AI within journalism. The most obvious reason for this is AI’s ability to make things up (“hallucinate“) — but there are other reasons too.
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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.
Continue readingMachine 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.
PS: The MA courses at BCU have an open day this month: you can register here.
The examples mentioned in the video include:
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.
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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.
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On Tuesday I will be hosting the award-winning investigative journalist and FOI campaigner Jenna Corderoy at the Lyra McKee Memorial Lecture. Ahead of the event, I asked Jenna about her tips on investigations, FOI, confidence, and the challenges facing the industry.
The story that I learned the most from was definitely our Clearing House investigation. Back in November 2020, we revealed the existence of a unit within the heart of government, which screened Freedom of Information (FOI) requests and instructed government departments on how to respond to requests. The unit circulated the names of requesters across Whitehall, notably the names of journalists.
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