towards editorial transparency in computational journalism

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)

Thank you!@_JAStark

starkja@umd.edu