towards editorial transparency in computational journalism
TRANSCRIPT
Towards Editorial Transparency in Computational Journalism
Jennifer A StarkNick Diakopoulos
The University of Maryland, College of Journalism, Computational Journalism Lab
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
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.
Why Share Our Work?
Benefits to yourself, fellow journalists, audience
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.
Case Study 1: Storytelling (Uber)
How?
Transparency promotes Accountability, Documentation, Further Storytelling
Share raw collected data: GitHub, Google Drive (consider size)
Open Source code sharing platform: GitHub, Jupyter
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
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
How?Case Study 2: Tool Making
(Twitter Bot)
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
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)
LicencesNobody 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/
Why Share Our Work?
Evidence difficult to measure at this time “IRL”
Sunlight LabsPolicy makers (eg Transport, AARP)
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”
Final Thoughts
Reinventing the wheel | Reuse code
Stack overflow for sharing code / solutions? http://area51.stackexchange.com/proposals/103335/data-journalism/
Data or file repository?: https://quiltdata.com (or something similar?? I have not tried this tool)
