Organisation: ABC News (Australian Broadcasting Corporation) (Australia)

Publication Date: 06/22/2017


A key component of what makes reporting long-running and complex news stories difficult is catering to an audience that has varied levels of background knowledge and exposure to the story so far. Because of this, each new article in a long running story will often include a lot of content which is unnecessary for many readers, yet vital to those readers who are new to a story. Our solution aims to serve stories that have been personalised to the knowledge level of the reader by replacing some background and context which would traditionally be written into new articles with a set of re-usable annotations, backgrounders and breakouts. This is done through explicit reader feedback in conjunction with heuristics based on what they’ve read previously. Our solution reduces cognitive load by removing superfluous content making the text much easier to scan without removing context that is important for specific readers who haven’t encountered it before. The prototype has been designed mobile-first and has been created to slot-in to any text based article. All components are AA compliant as per the Web Content Accessibility Guidelines and have also been designed to be colour-blindness friendly.

Technologies used for this project:

Annotation and backgrounder content is exposed via a JSON API and articles are progressively enhanced with the new functionality based on placement specified by the article’s author. The JSON API is written in Node JS using the Express framework. We use Markdown for authoring the background blocks and annotations.


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