Using visualisation on TV and video is very different to using charts and maps online. In video, the audience has very little time to absorb the information contained in the chart — so you need to get them to that information as quickly as possible.
Every bad example of charts in videos forgets this. And every good example uses two essential techniques: keeping things simple, and adding motion.
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Some essential reading by Agnes Stenbom Swedling explores how news organisations integrate AI into their workflows and the idea of the “human in the loop“. Many newsrooms, she points out, “are not optimised for what humans do best”, and so far the introduction of AI hasn’t involved a critical consideration of whether we want to embed those features in new systems, or rethink them:
Continue reading“What is being built – incrementally, often unintentionally – is a form of machine-centric hybridisation. Workflows are optimised for what machines do well: speed, scale, pattern recognition, cost efficiency. Humans are then positioned around those systems, adapting their tasks, roles, and decision-making to fit the logics of machines.
“The consequence is a subtle but significant inversion: rather than engaging in uniquely human activities, work is reorganised to fit machine-driven processes. And once that inversion is embedded at the infrastructural level, it becomes increasingly difficult to reverse.”
One of the most powerful ways to generate original journalism is to look at the systems behind stories — particularly the points where those systems fail.
For investigative work, those points are central. Surface-level scandals often stem from deeper systemic problems. So what tools do we have for recognising those patterns?
Donella Meadows’s classic book Thinking in Systems offers one: “system traps” — patterns that explain how systems get stuck, break down, or behave in ways nobody intends. They are “traps” because attempts to escape them often backfire.
In this post I’ll explain each trap, what it looks like in the wild, and how to use it as a lens for story ideas.
Continue readingAt this year’s Dataharvest I delivered a workshop on using Python in QGIS to automate the process of exporting maps for multiple locations. Here’s how to do it (you can find a GitHub repository with materials and links here).
Making a map for a story is cool — but what if you could make a map for every reader? Or if you’re working on a project involving teams in different regions or countries, what if you could give each one of those teams a map centred on their own patch?
Normally you would have to manually move the map to centre it on a key city, and then export an image. Then do it again and again and again for every area.
Luckily, QGIS has the ability to run code. And this is a great excuse to start using it.
In this latest post in the FAQ series, I am sharing some responses to a radio interview about AI’s impact on journalism.
It is certainly a problem yes, in three ways: it makes reporting harder, it makes it harder to support journalism financially, and it makes it harder for audiences to trust your reporting.
Continue readingFrom stories about political speech and song lyrics, to street names and social media chatter, data journalists now have a wide range of examples of text-as-data to draw inspiration and guidance from, while tools such as Pinpoint and NotebookLM are making text analysis easier than ever.
I compiled a list of over 200 pieces of data journalism where text or documents were used as sources. Quantification techniques ranged from counting the frequency of a single word and using Google’s ngram viewer, to machine learning and topic modelling.
Looking at those articles it’s clear that, once quantified, journalists tell the same stories about text as any other piece of data: using the seven most common angles.
But how those angles are used — and how often — is where it gets interesting…
PEER is a mnemonic (based on a previous post) for remembering the following four types of source:
Each type of source brings something different to the story: voices of power primarily (but not solely) answer questions about action: what was or is being done, what should or would be done about a particular issue. These are easily the most commonly quoted sources in news reporting.
People with expertise can answer the “why” and “how” questions — and are often more likely to speak to journalists — while those with experience can verify or validate (put a human face to) events. Representatives can speak to the wider impact or significance of an issue, or represent community sentiment about it.
Making each type of source explicit allows us to think about what those roles really mean — and identify less obvious ideas for sources with power, expertise, experience or representative qualities.
Continue readingFreedom of Information (FOI) requests are not only one of the best ways to get original and exclusive stories that set your reporting apart — they’re also a good way to develop core journalism habits like curiosity, scepticism, and creativity. Here are some tips on how to get started with FOI while developing those qualities.
Curiosity is the first quality I identified in my series on the 7 habits of successful journalists — and FOI is a great way to hone that.
One good way to get started with FOI is to identify an event or problem that you’ve read about, and get curious about it: how many times is that event happening? How much is that problem costing? These are perfect questions for FOI.
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A pirâmide invertida do jornalismo de dados mapeia o processo de utilização de dados na reportagem, desde a geração de ideias, passando pela limpeza, contextualização e combinação, até à comunicação. A fase final — a comunicação — apresenta uma série de opções: desde a visualização e sonificação até à personalização e ferramentas. Mas quais são as melhores práticas para cada uma?
(Também disponível em inglês, alemão e espanhol, russo e ucraniano).
A visualização é normalmente a forma mais rápida de comunicar os resultados do jornalismo de dados: ferramentas gratuitas como Datawrapper e Flourish muitas vezes exigem apenas que você carregue os seus dados e escolha entre várias opções de visualização.
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Os projetos de jornalismo de dados envolvem várias etapas, cada uma apresentando seus próprios desafios. Para ajudar a compreendê-las, criei o que chamei de ‘Pirâmide Invertida do Jornalismo de Dados’. Ela delineia as etapas que precisam ser consideradas à medida que a matéria avança desde a conceção inicial até a comunicação dos resultados, e como elas se relacionam entre si. Abaixo, explico cada etapa, identifico questões a considerar conforme o projeto avança e ofereço conselhos e dicas sobre como enfrentá-las.
(Também disponível em Inglês, Alemão, Espanhol, Finlandês, Russo e Ucraniano.)
O primeiro desafio que um jornalista enfrenta é conceber uma ideia viável para uma matéria baseada em dados.
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