Research:Exploring Wikimedia Donation Patterns
Every year, Wikimedia Foundation relies on fundraising campaigns to help maintain the services it provides to millions of users worldwide. However, despite a 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, therefore, seek to explore the following research question:
- RQ: Do donations across Wikipedia pages vary in some systematic ways?
In relation to this question we also investigate specific hypotheses based on measureable features of the pages:
- H1: Pages on more task-oriented topics attract more donations.
- H2: Pages on which users spent more time attract more donations.
- H3: Pages of higher quality attract more donations.
Insights gained 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, such as the topics, page quality, and the time users spend on the page predict the donation rates. Using page-level aggregated donation data from 2015 fundraising campaign for the French and English Wikipedias, we first collected the article content and meta-information for each page. To be able to model the impact of contents, we then categorized the articles into task-oriented and non-task oriented topics. We also collected the quality category for each article using the Objective Revision Evaluation Service (ORES). Finally, we collected the median and average time users spent on each page as a measure of engagement with the contents. Based on these article features we developed a number of models to predict the rates of donation on those pages.
- June 2016: Collect French Wikipedia content
- March 2017: Collect English Wikipedia content
- June 2017: Collect additional features - quality ratings, topic categories
- September 2017: Explore regression models with collected articles features to predict donation rates
- April 2018: Summarize findings in a report/publication
Policy, Ethics and Human Subjects Research
In our analysis, we have access to and use only the donation data aggregated per page, hence preserving the anonymity of the individual Wiki users.
Once your study completes, describe the results and 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