Yesterday I introduced three different roles: the sourcing expert, the beat curator, and the (data) analyst. Now it is time to look a little bit closer at how they should work together and how their work might fit into an overall process.
The Liquid Newsroom team manages and curates a constant flow of news from various sources: news organizations as much as tweets or blog posts from individuals. Not necessarily will the news enter the newsroom in chronological order. The sourcing expert is in charge to ensure a high quality of sources and she oversees the selection process of what should become part of the stream. A source can be completely unknown in the beginning. But the LNR will have a memory so that with more and more experience with a source, trust can grow over time. The little boxes in the process model on the left symbollize news items: e.g. one could be a tweet from someone in the Middle East, the second an article published by AP a few minutes ago and a third item might be a post on any of the most common social networks or video platforms (e.g. YouTube). Note: click the image to enlarge.
Therefore the whole LNR publishing process starts with some kind of input stream from a pool of sources. If you search for "Syria" on Google News you might get 28,000 results. But how do the articles differ? What additional information will a reader get if she doesn't only read "Activists demand ceasefire, aid access in Syria" (MSNBC) but also "'Friends of Syria' consider ultimatum to Assad"? (Seattle PI). The Google News search for "Syria" e.g. provides with an input stream. The beat curator decides about which parts of the input stream should become part of the topic (related news) stream. She also decides how to summarize or how to comment on news, tweets, etc. Readers should get an idea of what's happening taking into account different sources in one view. The Topic Stream is already the first stream to be published, whereas the input stream only represents LNR's internal flow of information.
Because the Topical Stream is published on a website, the (data) analyst can start to collect the first data in real-time. The analyst is keen to understand how the audience or the readers might interact with the news. How much will a news item be redistributed, retweeted, shared, liked? She's also in charge to manage the process of how to deal with comments and feedback, with concerns or questions. Most of the information from the feedback channel is invaluable. The feedback channel demands real communication: its main task is to foster two-way exchanges between the members of the newsroom, the readers AND the authors. What would happen if newsroom members would pass on the questions to the original authors of the news pieces, so that they can develope their story even further? - Based on the first analysis of readers' feedback the Edited Stream is also already a first mirror of the interest graph of the news consumer.
Enriched and Extended Stream are closely tied together. Both steps in the news editing value chain will help to enrich the news experience for the readers. Additional video footage or graphical material might help to gain an understanding of the story. The LNR instead will focus on the first three steps: Input Stream, Topic Stream and Edited Stream. The LNR team will pass the information on to another team of journalists and editors, who will work together closely to provide the reader with more background information etc. to be read on either appropriate devices (e.g. tablets) or any other publishing channel of interest.
If you like to know more or if you like to become part of the experiment, don't hesitate to get in touch with me - firstname.lastname@example.org