Using generative AI to help review your reporting: subbing, jargon, brevity and factchecking

Applications of genAI in the journalism process 
Pyramid with the third 'Production' tier highlighted: 

Identify jargon and bias; improve spelling, grammar, structure and brevity

In the fifth of a series of posts from a workshop at the Centre for Investigative Journalism Summer School, I look at using generative AI tools such as ChatGPT and Google Gemini to help with reviewing your work to identify ways it can be improved, from technical tweaks and tightening your writing to identifying jargon.

Having an editor makes you a better writer. At a basic level, an editor is able to look at your work with fresh eyes and without emotional attachment: they will not be reluctant to cut material just because it involved a lot of work, for example.

An editor should also be able to draw on more experience and knowledge — identifying mistakes and clarifying anything that isn’t clear.

But there are good editors, and there are bad editors. There are lazy editors who don’t care about what you’re trying to achieve, and there are editors with great empathy and attention to detail. There are editors who make you a better writer, and those who don’t.

Generative AI can be a bad editor. Ensuring it isn’t requires careful prompting and a focus on ensuring that it’s not just the content that improves, but you as a writer.

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Using generative AI as a scraping assistant

Applications of genAI in the journalism process 
Research
Pyramid with the second 'research' level highlighted: Scope diverse sources, explore documents, form advanced searches, and write/fix code for scraping and analysis
Scraping is part of the research phase of a project

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…

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Using generative AI as a spreadsheet and data cleaning assistant

Applications of genAI in the journalism process 
Research
Pyramid with the second 'research' level highlighted: Scope diverse sources, explore documents, form advanced searches, and write/fix code for scraping and analysis
Spreadsheet analysis is part of the research phase of a story

Generative 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.

Writing spreadsheet formulae with ChatGPT or other genAI tools

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Investigative journalism and ChatGPT: using generative AI for sourcing and story research

Applications of genAI in the journalism process 
Research
Pyramid with the second 'research' level highlighted: Scope diverse sources, explore documents, form advanced searches, and write/fix code for scraping and analysis
Generative AI can be used at all points in the journalism process: this post focuses on the research stage

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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Data journalism in the UK 2024 election: a numbers game

Bar chart: Case studies were used in only four of 50 articles by data journalists during the election
More than half of articles had no original quotes at all.

Data journalism during the election lacked case studies and quotes from those outside of the Westminster bubble, according to an analysis of over 50 pieces published during the election campaign.

But the rare exceptions offer examples of how we can do better election data journalism which sets the agenda and gives voters a louder voice.

Read the full analysis here. It is part of UK Election Analysis 2024: Media, Voters and the Campaign, a publication which “captures the immediate thoughts, reflections and early research insights on the 2024 UK General Election from the cutting edge of media and politics research.”

The analysis can also be compared to previous reflections in 2015 and 2010.


Investigative journalism and ChatGPT: using generative AI for story ideas

Applications of genAI in the journalism process
Pyramid with the bottom 'pre-production' layer highlighted: Idea generation and stimulation: identify and map systems and rules, apply brainstorming frameworks (iceberg model, 5 whys, 8 angles of data journalism). Planning.
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.

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VIDEO: AI in journalism: machine learning and natural language processing

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.

PS: The MA courses at BCU have an open day this month: you can register here.

The examples mentioned in the video include:

Investigative journalism’s AI challenges: accuracy and bias, explainability and resources

screenshots of guidelines on AI

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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AI in investigative journalism: mapping the field

screenshots of various examples of AI being used in journalism, including Serenata de Amor, Leprosy of the Land and The Implant Files

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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VIDEO: How can virtual reality, 360 degree video and augmented reality be used for journalism — and what are the challenges?

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.

The MA courses at BCU have an open day in June: you can register here.

You can find links to all of the examples used in the video on the YouTube page.

This video is shared as part of a series of video posts.