Research:Exploring Wikimedia Donation Patterns
Every year, Wikimedia Foundation relies on fundraising campaigns to help maintain the services it provide to millions of users worldwide. However, despite the large number of individuals who donate through these campaigns, these donors represent only a small percentage of Wikimedia users. Improving our understanding of donors and their behaviors to may result in more effective fundraising campaigns and limiting the burden of these campaigns on Wikipedia users.
Recent research suggests that different types of people may be drawn to different articles due to their personal reading interests. In other words, the articles themselves may provide some indicators about who the users are and their interests. It may be possible, then that (1) that fundraising campaigns on certain pages may be more successful (i.e. higher donation rate) due to the readers of the page and that (2) certain page/article properties can be used to predict the donation rate on the page. In this work, we seek to explore these two research questions. Insights gain can allow us better anticipate donation behaviors on individual pages, and offer more interesting and effective banner messages.
Our goal is to explore whether and how certain features of the wikipedia page predict the donation rates. Using donation data from previous year's fundraising campaign for the French Wikipedia, we will first collect the article content and meta-information for each page. We will then develop article features and test models to predict the rates of donation on those pages.
Please provide in this section a short timeline with the main milestones and deliverables (if any) for this project.
- June 2016: Collect French wikipedia content
- July 2017: Explore regression/machine learning models with text-based features to predict donation rates
Policy, Ethics and Human Subjects Research
It's very important that researchers do not disrupt Wikipedians' work. Please add to this section any consideration relevant to ethical implications of your project or references to Wikimedia policies, if applicable. If your study has been approved by an ethical committee or an institutional review board (IRB), please quote the corresponding reference and date of approval.
Once your study completes, describe the results an their implications here. Don't forget to make status=complete above when you are done.
Gary Hsieh, Jilin Chen, Jalal U. Mahmud, and Jeffrey Nichols. 2014. You read what you value: understanding personal values and reading interests. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). ACM, New York, NY, USA, 983-986. DOI=http://dx.doi.org/10.1145/2556288.2556995