Change Details

**What's requested:** We would like to evaluate the May edition of the #[[ https://meta.wikimedia.org/wiki/The_Wikipedia_Library/1Lib1Ref | 1Lib1Ref campaign ]]. We have data from the [[ http://hashtags.wmflabs.org/ | Hashtags ]] tool and would like to understand the breakdown of new, returning, and active editors from the participating projects. In this past campaign we also saw a higher than normal rate of users blocked - we would like to better understand how many editors were blocked (and in some cases later unblocked) and understand how many additions were reverted. From the Hashtags tool, we can provide a CSV file containing data on edits, including the usernames, pages, and projects for each edit made as part of the campaign which was tagged with #1lib1ref or #1bib1ref in the edit summary. In the past we have done simple analysis based primarily on querying the replica databases to check the activity levels of users per project. Reports for this analysis is linked below. **Why it's requested:** #1lib1ref is a campaign that we run every year to engage librarians in the Wikimedia movement. #1lib1ref May 2020 had an unusually high participation of new editors from the African context. We want to continue to monitor how that recruitment changes and effects the impact and relative effectiveness of the campaign, so that we can continue to make tweaks to make the initiative more effective and diverse. This analysis had been done by Sam on the Wikipedia Library team when 1Lib1Ref lived in that team, but we'd now like to figure out a more sustainable and thorough way of getting these metrics. **When it's requested:** We would prefer this data this quarter so that we can use it as part of thinking about the design and communication of the campaigns. However if it takes longer, that is fine. **Other helpful information:** - [[ https://docs.google.com/document/d/1KIyTX3txdJgvMg83wYHRkOkgioQtC0H_oFWWul2MKDs/edit | Report for January 2020 ]] - Sam on the Wikipedia Library team did this kind of analysis in the past and would be happy to answer questions about the data

**What's requested:** We would like to evaluate the May edition of the #[[ https://meta.wikimedia.org/wiki/The_Wikipedia_Library/1Lib1Ref | 1Lib1Ref campaign ]]. We have data from the [[ http://hashtags.wmflabs.org/ | Hashtags ]] tool and would like to understand the breakdown of new, returning, and active editors from the participating projects. In this past campaign we also saw a higher than normal rate of users blocked - we would like to better understand how many editors were blocked (and in some cases later unblocked) and understand how many additions were reverted. From the Hashtags tool, we can provide a CSV file containing data on edits, including the usernames, pages, and projects for each edit made as part of the campaign which was tagged with #1lib1ref or #1bib1ref in the edit summary. In the past we have done simple analysis based primarily on querying the replica databases to check the activity levels of users per project. Reports for this analysis is linked below. **Why it's requested:** #1lib1ref is a campaign that we run every year to engage librarians in the Wikimedia movement. #1lib1ref May 2020 had an unusually high participation of new editors from the African context. We want to continue to monitor how that recruitment changes and effects the impact and relative effectiveness of the campaign, so that we can continue to make tweaks to make the initiative more effective and diverse. This analysis had been done by Sam on the Wikipedia Library team when 1Lib1Ref lived in that team, but we'd now like to figure out a more sustainable and thorough way of getting these metrics. **When it's requested:** We would prefer this data this quarter so that we can use it as part of thinking about the design and communication of the campaigns. However if it takes longer, that is fine. **Other helpful information:** - [[ https://docs.google.com/document/d/1KIyTX3txdJgvMg83wYHRkOkgioQtC0H_oFWWul2MKDs/edit | Report for January 2020 ]] - Sam on the Wikipedia Library team did this kind of analysis in the past and would be happy to answer questions about the data **Delivery** [ ] editor cohort analysis for May campaign ( 5/15/2020-6/5/2020), using the same cohort definitions in [[ https://docs.google.com/document/d/1KIyTX3txdJgvMg83wYHRkOkgioQtC0H_oFWWul2MKDs/edit | Report for January 2020 ]] Cover below 10 wikis, and any other wikis which has more than 20-30 edits or more than 2-3 editors |Code|Language| |-----|----| |EN|English| |ES|Spanish| |CA|Catalan| |PL|Polish| |SV|Swedish| |SR|Serbian| |FR|French| |SW|Swahili| |IT|Italian| |HE|Hebrew| [ ] blocks on editors acquired in May campaign ( 5/15/2020-6/5/2020) [ ] reverted edits in May campaign ( 5/15/2020-6/5/2020) [ ] blocks on editors acquired in Jan campaign ( 1/15/2020-2/5/2020) [ ] reverted edits in Jan campaign ( 1/15/2020-2/5/2020)

**What's requested:** We would like to evaluate the May edition of the #[[ https://meta.wikimedia.org/wiki/The_Wikipedia_Library/1Lib1Ref | 1Lib1Ref campaign ]]. We have data from the [[ http://hashtags.wmflabs.org/ | Hashtags ]] tool and would like to understand the breakdown of new, returning, and active editors from the participating projects. In this past campaign we also saw a higher than normal rate of users blocked - we would like to better understand how many editors were blocked (and in some cases later unblocked) and understand how many additions were reverted. From the Hashtags tool, we can provide a CSV file containing data on edits, including the usernames, pages, and projects for each edit made as part of the campaign which was tagged with #1lib1ref or #1bib1ref in the edit summary. In the past we have done simple analysis based primarily on querying the replica databases to check the activity levels of users per project. Reports for this analysis is linked below. **Why it's requested:** #1lib1ref is a campaign that we run every year to engage librarians in the Wikimedia movement. #1lib1ref May 2020 had an unusually high participation of new editors from the African context. We want to continue to monitor how that recruitment changes and effects the impact and relative effectiveness of the campaign, so that we can continue to make tweaks to make the initiative more effective and diverse. This analysis had been done by Sam on the Wikipedia Library team when 1Lib1Ref lived in that team, but we'd now like to figure out a more sustainable and thorough way of getting these metrics. **When it's requested:** We would prefer this data this quarter so that we can use it as part of thinking about the design and communication of the campaigns. However if it takes longer, that is fine. **Other helpful information:** - [[ https://docs.google.com/document/d/1KIyTX3txdJgvMg83wYHRkOkgioQtC0H_oFWWul2MKDs/edit | Report for January 2020 ]] - Sam on the Wikipedia Library team did this kind of analysis in the past and would be happy to answer questions about the data **Delivery** [ ] editor cohort analysis for May campaign ( 5/15/2020-6/5/2020), using the same cohort definitions in [[ https://docs.google.com/document/d/1KIyTX3txdJgvMg83wYHRkOkgioQtC0H_oFWWul2MKDs/edit | Report for January 2020 ]] Cover below 10 wikis, and any other wikis which has more than 20-30 edits or more than 2-3 editors |Code|Language| |-----|----| |EN|English| |ES|Spanish| |CA|Catalan| |PL|Polish| |SV|Swedish| |SR|Serbian| |FR|French| |SW|Swahili| |IT|Italian| |HE|Hebrew| [ ] blocks on editors acquired in May campaign ( 5/15/2020-6/5/2020) [ ] reverted edits in May campaign ( 5/15/2020-6/5/2020) [ ] blocks on editors acquired in Jan campaign ( 1/15/2020-2/5/2020) [ ] reverted edits in Jan campaign ( 1/15/2020-2/5/2020)