We were inspired to develop FOLD during the 2014 crisis in Ukraine. As the story unfolded, we found ourselves struggling to understand what was happening. Neither of us knew much about the history of Crimea, or Ukrainian-Russian relations. This was frustrating, as we had difficulty interpreting the many news stories we read about the conflict.

Our story was not unique. It is hard to know where news will come from, so we often find ourselves lacking the background knowledge needed to interpret emerging but important stories. This summer was full of such examples. Beyond Ukraine, we read about the conflict in Gaza and the massive Ebola outbreak that continues to today — both extremely complex situations.

As readers, we often learn about news too late, once the stories have become “big” and the reporting focuses only on the most recent developments. But is this the way that news should be? Or is this the result of using ink and paper procedures in a world where digital technologies allow for more flexibility?

FOLD is a tool for reading, authoring, and publishing modular stories wrapped in contextual information like photos, maps, videos, tweets, interactive visualizations, and more. For this challenge, we were inspired to create a new feature enabling non-linear storytelling. FOLD is built around a fluid interface of “cards,” which can be used to compose parallel narratives. There are two types of cards: narrative cards, and context cards. Narrative cards are arranged vertically to form the "backbone" of a story. Context cards are positioned perpendicular to the narrative cards, providing an alternative axis for reading. Therefore, the main story can be read from top to bottom, while the context for each part of the story, which can be read selectively, is navigable from left to right. Links between cards allow readers to shape their own reading experience according to their own needs.

Inspired by this challenge, we wanted to create a tool that incorporates features for non-linear storytelling, but also meets the needs of everyday news readers. For example, because non-linearity can sometimes be confusing, we added a small map in the bottom righthand corner of the interface that helps the reader know where they are in the story.

FOLD is not just a novel reading experience, but also a new way to author non-linear stories. We are working on ways for authors to easily reuse and remix contextual information, and an engine for semi-automatically generating context. Therefore, FOLD is designed for both content producers, who can now add context to their stories more easily, and readers, who can now read stories without the need to search for the context elsewhere.

About us

Alexis Hope [alexishope.com] - Alexis is a designer, inventor, and researcher working with the Civic Media group at the MIT Media Lab. She uses methods from human-centered design to empower non-experts to learn, participate, and take action in important spheres of public and private life.

Kevin Hu [kevinzenghu.com] - Kevin is a software developer, inventor, and data scientist working with the Macro Connections group at the MIT Media Lab. He builds tools that enhance our ability to understand the digital world and democratize access to the insights contained within large datasets.

What’s next?

We are focusing on two things: on perfecting the authoring platform, which is up and running, but not yet ready for deployment, and on making FOLD mobile friendly. The authoring platform, or CMS, is a WYSIWYG (“What You See is What You Get”) interface that makes it easy for authors to compose non-linear stories.

The stories written with FOLD are embeddable, allowing people to compose on the FOLD website and still use their content on their personal websites (e.g. blogs). Larger organizations wanting more control or scale (such as large newspapers), can host their own FOLD deployments on their own server infrastructure.

We're huge proponents of open source software. FOLD is powered by OS tools like Meteor, Ubuntu and Nginx. As such, FOLD is developed under the permissive MIT License, and the source code is located on Github at: https://github.com/kevinzenghu/FOLD.

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