Hello everyone,


Apologies for cross-posting, and please forward to communities who you think will be interested.


In light of the movement’s increased focus on diversity and becoming essential infrastructure for diverse human knowledge, the Community Programs team at the WMF has begun investigating the use of content campaigns in the movement. Campaigns are some of the most reliable ways to create new content and introduce new contributors to the movement, but we have a lot of questions like:


What are the different steps that our community organizers have to go through in order to organize campaigns and contests such as Wiki Loves Monuments, Wikipedia Asian Month, #1lib1ref Wikidata Menu Challenge etc.? 


What are the tools that are being used in organizing these campaigns and contests such as PetScan, Listeria, CitationHunt, Fountain, wscontest tool, Outreach Dashboard etc.? 


During some initial research into the space, we have developed a draft Organizer Framework[1] that highlights key stages in organizing any campaign/contest and the documentation that we could find supporting those stages.


Now, we need your help us improve the framework so that we can help less experienced organizers understand how to better support content campaigns.


Please join us in discussing the framework and provide feedback on the talk page[2] or connect with us on the discuss forum.[3]


If you want to reach out directly, feel free to email us at astinson@wikimedia.org or sgill@wikimedia.org.


Thanks,


Alex Stinson and Satdeep Gill 


[1] https://meta.wikimedia.org/wiki/Campaigns/Organizer_Framework

[2] https://meta.wikimedia.org/wiki/Talk:Campaigns/Organizer_Framework

[3] https://discuss-space.wmflabs.org/t/feedback-on-content-campaigns-framework/1414



--
Alex Stinson 
Senior Program Strategist
Wikimedia Foundation
Twitter:@glamwiki/@sadads

Learn more about how the communities behind Wikipedia, Wikidata and other Wikimedia projects partner with cultural heritage organizations: https://outreach.wikimedia.org/wiki/GLAM