Bee Padalkar

For any FOSS community, newcomer onboarding is important but retaining them is critical. Since past one year, I have been working with the Fedora Community Operations team on community-oriented metrics and on improving contributor retention rates in Fedora. During this talk, I would like to share the findings from my work.

For any FOSS community, newcomer onboarding is important but retaining them is critical. Since past one year, I have been working with the Fedora Community Operations team on community-oriented metrics and on improving contributor retention rates in Fedora. During this talk, I would like to share the findings from my work.

This talk will discuss about the methods FOSS communities can adopt to improve contributor retention rates. I will explain different community-oriented metrics we use at Fedora to understand community health like contributor engagement metrics, contributor onboarding and retention rates etc. I will then share some insights derived from finding common patterns in contribution activity of long-term active contributors i.e. Are long-term active contributors involved in many different areas of their projects? What are some good measures to predict longetivity of contributors ? What is the magic element X that makes people stay ? I will talk on how a community can apply the insights generated from these metrics to devise methods/guidelines to improve contributor retention rates and how an individual interested in contributing can ensure their prolonged interest in the FOSS project/organization.

In the end, I will also share my experience as a newcomer and contributor to Community Operations team of Fedora and have a brief QnA session about the steps FOSS communities apart from Fedora are taking to improve their contributor retention rates.

I have been involved with Fedora Community Operations Team where I mostly contribute on community-metrics related tasks. This talk will be about a NLP-based tool I have built for Fedora wiki to make wiki-gardening tasks easier for contributors, the methods I used for building it and how it can be scaled to any other wiki.

Fedora wiki is a community documentation space about different projects, initiatives, contributors in Fedora Project. However, due to large size of the project, the Fedora wiki has been growing in size and has become difficult to manage. As a result, wiki gardening tasks were born. These tasks generally involve not only updating current information on wiki pages but also identifying pages with redundant information to merge them, marking pages with old and outdated information accordingly etc.Most of these wiki gardening tasks are a part of one-day FADs, hackathons or sprints and suitable for new contributors looking to contribute to the project as they have a low entry-barrier. While there has been a category traditionally to mark wiki pages which need to be worked on, this task is mostly manual(needs to be done by some contributor prior to the hackathon or event) and also doesn't cover the whole wiki as it is manually difficult due to it's large size.

My tool uses Natural Language Processing-based techniques to 1.Identify pages with redundant information so that contributors can merge/delete them 2.Identify pages in a specific category/topic which need to be worked on. 3.Identify old pages which might need to be updated or categorized as out-of-date

To make the tool faster, I have used Parallel Processing techniques. In this talk, I would like to describe the tool and it's functionality, the methods I used for building it and how it can be scaled to any other wiki. I would also like to hear feedback on other wiki gardening tasks this tool could possibly be used for, and how to scale it to automatically merge pages with redundant information.