How can technology help journalists make sense of complex issues and explain them to the public in a clear, understandable manner?
Last year, Jay Rosen’s journalism students spent an entire semester researching and making explanations in partnership with ProPublica, a non-profit newsroom which focuses on investigative journalism. The class did amazing work to highlight notable examples and develop their own “explainers,” essential background knowledge to help people follow events and trends in the news. One of my favorite examples is this project from 2011, where students redesigned the same ProPublica background article as a video, a podcast, and an FAQ.
NYU’s Explainer class focused especially on two things: presentation and conversation. They talked to cognitive psychologists like George Lakoff to learn how audiences take in what we read. They highlighted numerous presentation examples — videos, timelines, infographics, mini-sites, aggregators, podcasts, interactive guides, flowcharts, and even a picture book by Google! The class at NYU also pointed out that explaining is often a conversation. In their journalist’s guide to developing FAQs, the class suggests techniques for discovering what people need to know. I loved their advice on listening to readers.
Where can we innovate?
This term, I’m taking Ethan Zuckerman’s Participatory News class from the point of view of a technology designer who wants to build tools to support great journalism. As we write stories and review other people’s work, we’re keeping our eyes open for parts of the process which technology can improve. As a startup guy, I also keep an eye open for alternative business models. Here are my top tech recommendations for supporting better explainers:
1. Peer Production
Jay points out in his “National Explainer“ essay that it’s OK to start with the clueless journalist. When learning how to explain something, our initial ignorance helps us appreciate where our audiences are coming from. This approach assumes that a professional journalist is doing the work; where else might we find uninformed, capable people to develop explainers?
I think we should take inspiration from Wikipedia to develop strategies for peer production of explanatory journalism, especially for issues that journalists can’t or don’t cover. Online communities like Metafilter have proven their ability to cooperate on investigations on occasion. How can we extend that to explanations? We could also draw inspiration from Instructables and CommonCraft, online communities of people who share video instructions and explanations.
Building online communities is hard. Instead of developing an “explainer” community, I would build a toolkit which existing communities can use when they feel the need to investigate and explain an issue.
2. Finding Voices
Many of the explainers in Jay’s class involve narrative. “The Giant Pool of Money“ succeeded because This American Life found the right cast of characters to illustrate a complex issue. But finding the right people is really hard, especially if you’re not a mainstream media organization. Source databases such as The Public Insight Network can help, but it’s a closed system unavailable outside of newsrooms. Social media networks through groups like Global Voices get us part of the way, but only as far as the people who might know those we’re looking for.
I’m not sure the crowd can help here. In many cases, the people you want to interview might not be outspoken online. Instead, I would develop tools and research practices for individuals or small teams to find representative voices. Perhaps the tool could offer encouragement and ideas for following the trail from an effect to an individual.
We could support one workflow in particular. Given a set of articles which are already about a topic, we could automatically extract the names of the organizations and individuals who are quoted and referred to, creating a quick map of the issue in the media. A canny storyteller might be able to spot gaps in the story or simply remix existing material into an explainer.
3. Organizing Research
Explainers are by definition hard to organize and research. They’re the messy, complicated issues that don’t appear to make much sense. Often the story arc isn’t apparent until partway through the project. It can become easy to get lost in the forest of information. As the pile of research grows, it can be difficult to follow the structure of a complex system or pull together the information you need for that next interview.
The most widely used writing tools are terrible at helping people organize and understand their information. I have written elsewhere about my use of software like Eastgate’s Tinderbox to organize research around a complex issue. I think we need more of that kind of software (James Fallows’ article on “Mac Programs that Come with Thinking Caps On“ is a great place to start).
4. Rhetorical Forms
All storytelling on computers is in its early stages; we haven’t agreed on very many common literary forms. Beyond the FAQ, the Timeline, and the illustrated lecture, most explainers require a custom rhetorical form. That’s bad for anyone who wants to put a deadline on a project.
That’s why I love The Explainer Awards that Jay and his students held. Awards are a great way to create norms and highlight innovation — they have been an effective model as far back as 5th century Athens. But we need to take this further. An effective awards program would bring together finalists in each category to discuss common challenges and build technologies to solve those problems.
Why not re-imagine explaining as a social movement rather than content production? Some of the best explaining comes from a two-way conversation, not a piece of content. We could start a service called Meet the News, a geolocated service which invites anyone to have coffee with someone affected by a news story. Participants could pay for the coffee and might be expected to contribute back to the community with a few paragraphs about the conversation, just like couch-surfing reviews. It could be a human library for the news.
Do you have more tech ideas for explanatory journalism? Let us know in the comments!
A version of this post first appeared on MIT Civic Media Center’s blog.