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Urbs Media and robot journalism

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Presentation by Alan Renwick of Urbs Media at the Data Journalism UK conference, Birmingham in December 2017

Published in: Data & Analytics
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Urbs Media and robot journalism

  1. 1. Urbs Media is a tech-driven editorial agency aiming to scale news production. We organise open data to uncover news stories then write them using technologies that enable automated production & distribution of multiple, localised versions. Global Editors Network named us ‘Runners-Up’ in this year’s Start Up programme Our RADAR project with PA received €706,000 funding from Google DNI Summer 2017
  2. 2. Social Problem An ocean of important open data unseen by citizens. Democratic bodies not held to account. Industry Problem A local media ecosystem facing growing demand for content but shrinking resource. Solution Systematically mine open data to find important, fact-based stories; harness automation to make this economic. Empower reporters Empower citizens
  3. 3. Scalable input – stories from data to empower local communities “Our local news site? Today, a random series of events. Tomorrow, another series of random events, unrelated to what happened today. No attempt to make sense of how the city works, or why.” 3 Building a news service from data as the input means: ‒ Stories are factual and verifiable ‒ Data points link over time to develop a narrative arc ‒ Different data points combine to explain what and why ‒ Scalability of data drives coverage and comparability (do it once for everywhere)
  4. 4. Proof of concept - London data to test a city-wide site 4 “Urbs.London is a good example of where we envisage journalism needs to go: Investigating the much richer data hubs that have become available – in transport, health, public policies, crime, jobs, and so on – and structuring relevant narrative, not just one-off stories to inform local audiences.” Enders Analysis, November 2015
  5. 5. Production scale: an editorial driven use of NLG – writing stories (leverage), not reports (velocity) ‒ Identify newsworthy localised datasets ‒ Mine data for story lines ‒ Structure data to enable automation ‒ Load data into Natural Language Generation tool ‒ Write story templates to cover all eventualities of story ‒ Template branching to cover different angles/emphasis ‒ Feed data through template to produce multiple local stories 5
  6. 6. 6 6 Scaled production needs an open market
  7. 7. Distribution scale - RADAR ‒ In September 2017 we started RADAR in partnership with the Press Association. ‒ Mass localisation service rolled out to the news ecosystem, with the build of a full end-to-end workflow from source data to distribution. 7 ‒ Launch market UK - RADAR will supply a daily diet of stories for each local market, distributed via a new channel. ‒ Full production capacity by February 2018; full distribution by May 2018.
  8. 8. Data tools to organise and find meaning in data Data Sources Data Sources Data Sources Data Sources Data Sources Data Sources NLG Multiple Story Production (Text) Visuals Story Ingestion, Targeting & Distribution Media Outlets Media Outlets Media Outlets Media Outlets Media Outlets Media Outlets Media Outlets Media Outlets Collection of inputs Content Package Production Deliver Content Findkeydatasources Identify,write&templatestory Video Samplecheckingfinishedpackage RADAR builds the Urbs journalists’ workbench out into a full-end-to end market service Core Human Editorial Tasks Key Automation/AI Developments
  9. 9. Data Driven News – Market Readiness 2017 (Top 20) Source: https://index.okfn.org/dataset/ Note: Chart shows top 20 markets (combined score) Global Open Data Index – Markets Ranked by Overall Score (Bubble Size), National Statistics and Administrative Boundaries (2016-17, All Ranked 0 to 100) 10 markets have mature open data supply – with more close behind
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