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Towards editorial transparency in computational journalism

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This goes together with a research paper also uploaded here describing practical steps to transparency in computational journalism with two case studies.

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Towards editorial transparency in computational journalism

  1. 1. Towards Editorial Transparency in Computational Journalism Jennifer A Stark Nick Diakopoulos The University of Maryland, College of Journalism, Computational Journalism Lab
  2. 2. What do we mean by “Transparency” ? “the ways in which people both inside and external to journalism are given a chance to monitor, check, criticize and even intervene in the journalistic process.” Deuze, M. 2005. What is journalism?: Professional identity and ideology of journalists reconsidered. Journalism. 6, 4 (2005), 442–464
  3. 3. What do we mean by “Transparency” ? Storytelling: Make the steps / data used to create your story visible to the audience. Tool making: Sharing the code with thorough documentation.
  4. 4. Why Share Our Work? Benefits to yourself, fellow journalists, audience
  5. 5. Accountability Document Process Stimulate Alternative Stories / viewpoints Double check data, code, analysis, and conclusions / interpretation Facilitate future work / future you / fellow journalists / field Novel work, or extensions to your original work.
  6. 6. Case Study 1: Storytelling (Uber) How?
  7. 7. Transparency promotes Accountability, Documentation, Further Storytelling Share raw collected data: GitHub, Google Drive (consider size) Open Source code sharing platform: GitHub, Jupyter
  8. 8. Transparency promotes Accountability, Documentation, Further Storytelling Share raw collected data: GitHub, Google Drive (consider size) Open Source code sharing platform: GitHub, Jupyter Project and Code Documentation: README.md APIs
  9. 9. Transparency promotes Accountability, Documentation, Further Storytelling Share raw collected data: Google Drive (consider size) Open Source code sharing platform: GitHub, Jupyter Project and Code Documentation: README.md Accountability: share data collection / processing / wrangling and analysis Interim processed data: .csv files Replicability: programmatic steps where possible APIs
  10. 10. How? Case Study 2: Tool Making (Twitter Bot)
  11. 11. Twitter Bot: Transparancy promotes accessibilityOpen Source code sharing platform: GitHub, Jupyter Project and Code Documentation: README.md Language / platform agnostic: configuration file • How much to parameterize? • Case-by-case uniqueness? Instructions within code and README documentation Comment APIs
  12. 12. Documentation! Takes longer than you think Consider it an investment Documentation within code Documentation in GitHub repository (README.md) Reciprocal links between news article and GitHub repository Links to reference material (eg APIs, preceding work)
  13. 13. Licences Nobody should use your Code or Data if it is not licenced Code licences https://opensource.org/licenses Data licences http://opendatacommons.org/about/ Multiple licences http://choosealicense.com/non-software/
  14. 14. Why Share Our Work? Evidence difficult to measure at this time “IRL”
  15. 15. Sunlight Labs Policy makers (eg Transport, AARP)
  16. 16. Hobbyists / Individuals Kate Rabinowitz – “Civic data scientist” http://www.datalensdc.com/index2.html About: “DataLensDC has been featured in The Washingtonian, The Atlantic's CityLab, Washington City Paper, WJLA ABC 7 News, and more”
  17. 17. Final Thoughts Reinventing the wheel | Reuse code Stack overflow for sharing code / solutions? http://area51.stackexchange.com/proposals/1 03335/data-journalism/ Data or file repository?: https://quiltdata.com (or something similar?? I have not tried this tool)
  18. 18. Thank you! @_JAStark starkja@umd.edu

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