Wikimedia Research/Showcase/Archive/2023/01

January 2023[edit]

Time: 9:30am PDT / 12:30pm EDT Find your local time here
Theme: Editor Retention

January 18, 2023 Video: YouTube

Learning to Predict the Departure Dynamics of Wikidata Editors

By Guangyuan Piao, Maynooth University

Wikidata as one of the largest open collaborative knowledge bases has drawn much attention from researchers and practitioners since its launch in 2012. As it is collaboratively developed and maintained by a community of a great number of volunteer editors, understanding and predicting the departure dynamics of those editors are crucial but have not been studied extensively in previous works. In this paper, we investigate the synergistic effect of two different types of features: statistical and pattern-based ones with DeepFM as our classification model which has not been explored in a similar context and problem for predicting whether a Wikidata editor will stay or leave the platform. Our experimental results show that using the two sets of features with DeepFM provides the best performance regarding AUROC (0.9561) and F1 score (0.8843), and achieves substantial improvement compared to using either of the sets of features and over a wide range of baselines.